From 6bba6715f5e97dbb615ed2ff0ff7a0d8ee191db3 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 19 Nov 2019 04:43:35 -0500 Subject: [PATCH 01/21] compare_plots function --- .DS_Store | Bin 14340 -> 14340 bytes R/ggconcurve.R | 73 +++---- R/plot_compare.R | 215 +++++++++++++++++++ R/plot_concurve.R | 4 +- README.Rmd | 1 - README.md | 2 - docs/index.html | 8 +- docs/news/index.html | 2 +- docs/reference/ggconcurve-1.png | Bin 150938 -> 150959 bytes docs/reference/ggconcurve-2.png | Bin 147767 -> 147544 bytes docs/reference/plot_concurve-1.png | Bin 122048 -> 122116 bytes pkgdown/favicon/apple-touch-icon-120x120.png | Bin 5839 -> 5839 bytes pkgdown/favicon/apple-touch-icon-152x152.png | Bin 7448 -> 7448 bytes pkgdown/favicon/apple-touch-icon-180x180.png | Bin 8818 -> 8818 bytes pkgdown/favicon/apple-touch-icon-60x60.png | Bin 2862 -> 2862 bytes pkgdown/favicon/apple-touch-icon-76x76.png | Bin 3660 -> 3660 bytes pkgdown/favicon/apple-touch-icon.png | Bin 8818 -> 8818 bytes pkgdown/favicon/favicon-16x16.png | Bin 1144 -> 1144 bytes pkgdown/favicon/favicon-32x32.png | Bin 1706 -> 1706 bytes 19 files changed, 257 insertions(+), 48 deletions(-) create mode 100644 R/plot_compare.R diff --git a/.DS_Store b/.DS_Store index 05e551430992451b16774b673cb82126cd4a81e9..5aa1a03d548de3ad07409fb08057de3f693c3854 100644 GIT binary patch delta 894 zcmeH^KT{Jy7{;HMAS8mYAp`|v{Bst};3$PHLyUz+ie!=$IRP_wmrYn*c5n7B#MsbK zJI>ho0c^Bk(m3OconOMnFJNJ1DPHd4fRxs5^ZxeT_jz{b*(3kRe_f`#$CXdlXn8Pw zJxg{5cTmM11OkN6FzA=0_FSo501QZNM0NEfUJ(IPvBHlgHJ(M>jg%bboa+5x=0+}c zD7*td@uJVJFGUcpuYY5V#!}-KCMMIFi&N8=W@cG-E|;Cp=JWZ5%U2ecmaaBg&UL)! z9izLd^*~E)in7Za!qciIUh+oBUxY(wvbl3}Ro;4f+N#$3el#?H+KJ5`8qU>r$B0(B z-41xzIKy_6%?6&)QdV>zLZOwt6YS|;DdhVl)z)uTmKP8II&avHF^=l+Vt-dUE|+Ch z)M2RihfYIf^y-S?2ZHzFduqPp3DvPSf5dshDpdZ!Y61*a0zjIPRVXC8y3&egGFHLa z8Xc#xhWmJoTJ(cGyuw?&!$*9PXR2kP`e)i7_$r@5dpJ>Ap`@nUnZaiv%wlh1+xb*_5_pO5LS~< K5(l%GC#nL|^AW`W diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 60c6c24..763016c 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -1,17 +1,20 @@ -ggconcurve <- function(type = "consonance", data, measure = "default", nullvalue = "absent", position = "pyramid", +ggconcurve <- function(data, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Consonance Function", subtitle = "The function contains consonance intervals at every level.", xaxis = "Range of Values", yaxis = "P-value", color = "#555555", fill = "#239a98") { + if (is.data.frame(data) != TRUE) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (ncol(data) != 5) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + + # Consonance Function + if (type == "consonance") { - if (is.data.frame(data) != TRUE) { - stop("Error: 'x' must be a data frame from 'concurve'.") - } - if (ncol(data) != 5) { - stop("Error: 'x' must be a data frame from 'concurve'.") - } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } @@ -55,22 +58,29 @@ ggconcurve <- function(type = "consonance", data, measure = "default", nullvalue x = xaxis, y = yaxis ) + - theme_light() + + theme_bw() + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), axis.title.x = element_text(size = 12), - axis.title.y = element_text(size = 12) - ) + { + axis.title.y = element_text(size = 12), + text = element_text(size = 11) + ) + + { if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) - } + { + } + + { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) - } + { + } + + { if (position == "inverted") { scale_y_reverse( breaks = seq(0, 1, .05), sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) ) } - } + { + } + + { if (position == "pyramid") { scale_y_continuous( breaks = seq(0, 1, .05), @@ -78,33 +88,24 @@ ggconcurve <- function(type = "consonance", data, measure = "default", nullvalue ) } } + - theme(text = element_text(size = 11)) + - theme( - plot.title = element_text(size = 12), - plot.subtitle = element_text(size = 11) - ) + if (nullvalue == "present") { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .6 + color = "#990000", alpha = 0.3, size = .8 ) } else if (measure == "ratio") { annotate("segment", x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .6 + color = "#990000", alpha = 0.3, size = .8 ) } } else if (nullvalue == "absent") { } + + # Surprisal function } else if (type == "surprisal") { - if (is.data.frame(data) != TRUE) { - stop("Error: 'data' must be a data frame from 'concurve'") - } - if (ncol(data) != 5) { - stop("Error: 'data' must be a data frame from 'concurve'") - } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } @@ -142,21 +143,21 @@ ggconcurve <- function(type = "consonance", data, measure = "default", nullvalue x = xaxis, y = "S-value (bits of information)" ) + - theme_light() + + theme_bw() + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), axis.title.x = element_text(size = 12), - axis.title.y = element_text(size = 12) - ) + { + axis.title.y = element_text(size = 12), + text = element_text(size = 11) + ) + + { if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) - } + { + } + + { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + - scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) + - theme(text = element_text(size = 11)) + - theme( - plot.title = element_text(size = 12), - plot.subtitle = element_text(size = 11) - ) + scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) } } diff --git a/R/plot_compare.R b/R/plot_compare.R new file mode 100644 index 0000000..713fa76 --- /dev/null +++ b/R/plot_compare.R @@ -0,0 +1,215 @@ +plot_compare <- function(type = "consonance", data1, data2, measure = "default", nullvalue = "absent", position = "pyramid", + title = "Consonance Function", + subtitle = "The function contains consonance intervals at every level.", + xaxis = "Range of Values", + yaxis = "P-value", + color = "#555555", + fill1 = "#239a98", + fill2 = "#d46c5b") { + if (is.data.frame(data1) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data1) != 5) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (is.data.frame(data2) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data2) != 5) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + + + # Consonance Function ----------------------------------------------------- + + + if (type == "consonance") { + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.character(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + } + if (is.character(position) != TRUE) { + stop("Error: 'position' must be a string such as 'pyramid' or 'inverted'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(xaxis) != TRUE) { + stop("Error: 'xaxis' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill1) != TRUE) { + stop("Error: 'fill1' must be a string for the color.") + } + if (is.character(fill2) != TRUE) { + stop("Error: 'fill2' must be a string for the color.") + } + ggplot(data = data1) + + geom_point(aes(x = lower.limit, y = pvalue), + color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_point(aes(x = upper.limit, y = pvalue), + color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_ribbon(aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue), + fill = fill1, alpha = 0.30 + ) + + geom_ribbon(aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue), + fill = fill1, alpha = 0.30 + ) + + geom_point( + data = data2, aes(x = lower.limit, y = pvalue), + color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_point( + data = data2, aes(x = upper.limit, y = pvalue), + color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_ribbon( + data = data2, aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue), + fill = fill2, alpha = 0.30 + ) + + geom_ribbon( + data = data2, aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue), + fill = fill2, alpha = 0.30 + ) + + theme_bw() + + labs( + title = title, + subtitle = subtitle, + x = xaxis, + y = yaxis, + color = fill1 + ) + + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11), + legend.position = "bottom" + ) + + { + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + } + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + { + if (position == "inverted") { + scale_y_reverse( + breaks = seq(0, 1, .05), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + ) + } + } + + { + if (position == "pyramid") { + scale_y_continuous( + breaks = seq(0, 1, .05), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + ) + } + } + + if (nullvalue == "present") { + if (measure == "default") { + annotate("segment", + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .8 + ) + } else if (measure == "ratio") { + annotate("segment", + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .8 + ) + } + } + else if (nullvalue == "absent") { + } + + + # Surprisal Function ------------------------------------------------------ + } else if (type == "surprisal") { + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(xaxis) != TRUE) { + stop("Error: 'xaxis' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill1) != TRUE) { + stop("Error: 'fill1' must be a string for the color.") + } + if (is.character(fill2) != TRUE) { + stop("Error: 'fill2' must be a string for the color.") + } + ggplot(data = data1) + + geom_point(aes(x = lower.limit, y = svalue), + color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_point(aes(x = upper.limit, y = svalue), + color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_ribbon(aes(x = lower.limit, ymin = max(svalue), ymax = svalue), + fill = fill1, alpha = 0.30 + ) + + geom_ribbon(aes(x = upper.limit, ymin = max(svalue), ymax = svalue), + fill = fill1, alpha = 0.30 + ) + + geom_point( + data = data2, aes(x = lower.limit, y = svalue), + color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_point( + data = data2, aes(x = upper.limit, y = svalue), + color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + ) + + geom_ribbon( + data = data2, aes(x = lower.limit, ymin = max(svalue), ymax = svalue), + fill = fill2, alpha = 0.30 + ) + + geom_ribbon( + data = data2, aes(x = upper.limit, ymin = max(svalue), ymax = svalue), + fill = fill2, alpha = 0.30 + ) + + labs( + title = title, + subtitle = subtitle, + x = xaxis, + y = "S-value (bits of information)" + ) + + theme_bw() + + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11) + ) + + { + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + } + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) + } +} + +# RMD Check +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.level", "pvalue", "svalue")) diff --git a/R/plot_concurve.R b/R/plot_concurve.R index c5ada9d..b86598f 100644 --- a/R/plot_concurve.R +++ b/R/plot_concurve.R @@ -1,5 +1,5 @@ -plot_concurve <- function(type = "consonance", - data, +plot_concurve <- function(data, + type = "consonance", measure = "default", intervals = FALSE, title = "Consonance Function", diff --git a/README.Rmd b/README.Rmd index c286d42..179cdad 100644 --- a/README.Rmd +++ b/README.Rmd @@ -1,5 +1,4 @@ --- -title: concurve output: github_document --- concurve | Graph Interval Functions diff --git a/README.md b/README.md index 22d3241..3ce1c7e 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,3 @@ -concurve -================ # concurve | Graph Interval Functions diff --git a/docs/index.html b/docs/index.html index 8473efc..644dc2f 100644 --- a/docs/index.html +++ b/docs/index.html @@ -101,14 +101,10 @@
-
- -
-

+

In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

diff --git a/docs/news/index.html b/docs/news/index.html index 8e666d7..29a979e 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -150,7 +150,7 @@

Changelog

-concurve 2.1.0 Unreleased +concurve 2.1.0 2019-09-19

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  • diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 9a55734..de0b804 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -84,7 +84,7 @@ concurve - 2.1.0 + 2.2.0
    diff --git a/docs/articles/examples.html b/docs/articles/examples.html index c6b587d..ca289e2 100644 --- a/docs/articles/examples.html +++ b/docs/articles/examples.html @@ -41,7 +41,7 @@ concurve - 2.1.0 + 2.2.0
    diff --git a/docs/articles/index.html b/docs/articles/index.html index 736b1a9..91881a5 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -84,7 +84,7 @@ concurve - 2.1.0 + 2.2.0
    diff --git a/docs/articles/stata.html b/docs/articles/stata.html index bb590ce..37c5ef4 100644 --- a/docs/articles/stata.html +++ b/docs/articles/stata.html @@ -41,7 +41,7 @@ concurve - 2.1.0 + 2.2.0 diff --git a/docs/authors.html b/docs/authors.html index 42246db..382073a 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -84,7 +84,7 @@ concurve - 2.1.0 + 2.2.0 @@ -149,16 +149,15 @@

    Citation

    Chow Z, Vigotsky A (2019). -concurve: Computes and Plots Consonance (Confidence) Intervals, P-Values, and S-Values to Form Consonance and Surprisal Functions. -doi: 10.5281/zenodo.1308151, R package version 2.1.0, https://CRAN.R-project.org/package=concurve. +concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. +R package version 2.2.0, https://CRAN.R-project.org/package=concurve.

    @Manual{,
    -  title = {{concurve}: Computes and Plots Consonance (Confidence) Intervals, P-Values, and S-Values to Form Consonance and Surprisal Functions},
    +  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
       author = {Zad R. Chow and Andrew D. Vigotsky},
       year = {2019},
    -  note = {R package version 2.1.0},
    +  note = {R package version 2.2.0},
       url = {https://CRAN.R-project.org/package=concurve},
    -  doi = {10.5281/zenodo.1308151},
     }
    @@ -216,18 +216,18 @@

    Examp bob <- curve_mean(GroupA, GroupB, RandomData) tibble::tibble(bob)
    #> # A tibble: 10,000 x 1 -#> bob$lower.limit $upper.limit $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.919 -0.919 0 1 0 -#> 2 -0.919 -0.918 0.0001 1.000 0.000144 -#> 3 -0.920 -0.917 0.0002 1.000 0.000289 -#> 4 -0.920 -0.917 0.000300 1.000 0.000433 -#> 5 -0.921 -0.916 0.0004 1.000 0.000577 -#> 6 -0.921 -0.916 0.0005 1.000 0.000722 -#> 7 -0.922 -0.915 0.000600 0.999 0.000866 -#> 8 -0.922 -0.915 0.0007 0.999 0.00101 -#> 9 -0.923 -0.914 0.0008 0.999 0.00115 -#> 10 -0.923 -0.914 0.0009 0.999 0.00130 +#> bob$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.919 -0.919 1 0 1 0 +#> 2 -0.919 -0.918 0.999 0.0001 1.000 0.000144 +#> 3 -0.920 -0.917 0.998 0.0002 1.000 0.000289 +#> 4 -0.920 -0.917 0.997 0.000300 1.000 0.000433 +#> 5 -0.921 -0.916 0.995 0.0004 1.000 0.000577 +#> 6 -0.921 -0.916 0.994 0.0005 1.000 0.000722 +#> 7 -0.922 -0.915 0.993 0.000600 0.999 0.000866 +#> 8 -0.922 -0.915 0.992 0.0007 0.999 0.00101 +#> 9 -0.923 -0.914 0.991 0.0008 0.999 0.00115 +#> 10 -0.923 -0.914 0.990 0.0009 0.999 0.00130 #> # … with 9,990 more rows
    diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html index f690568..687e346 100644 --- a/docs/reference/curve_meta.html +++ b/docs/reference/curve_meta.html @@ -87,7 +87,7 @@ concurve - 2.1.0 + 2.2.0 @@ -242,18 +242,18 @@

    Examp metaf <- curve_meta(res) tibble::tibble(metaf)
    #> # A tibble: 10,000 x 1 -#> metaf$lower.limit $upper.limit $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.120 0.120 0 1 0 -#> 2 0.120 0.121 0.0001 1.000 0.000144 -#> 3 0.120 0.121 0.0002 1.000 0.000289 -#> 4 0.120 0.121 0.000300 1.000 0.000433 -#> 5 0.120 0.121 0.0004 1.000 0.000577 -#> 6 0.120 0.121 0.0005 1.000 0.000722 -#> 7 0.120 0.121 0.000600 0.999 0.000866 -#> 8 0.120 0.121 0.0007 0.999 0.00101 -#> 9 0.120 0.121 0.0008 0.999 0.00115 -#> 10 0.120 0.121 0.0009 0.999 0.00130 +#> metaf$lower.limit $upper.limit $intrvl.level $pvalue $svalue $limit.ratio +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.120 0.120 0 1 0 1 +#> 2 0.120 0.121 0.0001 1.000 0.000144 1.00 +#> 3 0.120 0.121 0.0002 1.000 0.000289 1.00 +#> 4 0.120 0.121 0.000300 1.000 0.000433 1.00 +#> 5 0.120 0.121 0.0004 1.000 0.000577 1.00 +#> 6 0.120 0.121 0.0005 1.000 0.000722 1.00 +#> 7 0.120 0.121 0.000600 0.999 0.000866 1.00 +#> 8 0.120 0.121 0.0007 0.999 0.00101 1.00 +#> 9 0.120 0.121 0.0008 0.999 0.00115 1.00 +#> 10 0.120 0.121 0.0009 0.999 0.00130 1.00 #> # … with 9,990 more rows
    @@ -204,18 +204,18 @@

    Examp df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") tibble::tibble(df)
    #> # A tibble: 9,999 x 1 -#> df$lower.limit $upper.limit $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.624 4.15 1.000 0.0001000 13.3 -#> 2 0.651 3.98 1.000 0.000200 12.3 -#> 3 0.667 3.88 1.000 0.000300 11.7 -#> 4 0.680 3.81 1.000 0.000400 11.3 -#> 5 0.690 3.76 1.000 0.000500 11.0 -#> 6 0.698 3.71 0.999 0.0006 10.7 -#> 7 0.705 3.68 0.999 0.0007 10.5 -#> 8 0.712 3.64 0.999 0.0008 10.3 -#> 9 0.717 3.61 0.999 0.0009 10.1 -#> 10 0.722 3.59 0.999 0.001 9.97 +#> df$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.624 4.15 6.65 1.000 0.0001000 13.3 +#> 2 0.651 3.98 6.12 1.000 0.000200 12.3 +#> 3 0.667 3.88 5.82 1.000 0.000300 11.7 +#> 4 0.680 3.81 5.61 1.000 0.000400 11.3 +#> 5 0.690 3.76 5.45 1.000 0.000500 11.0 +#> 6 0.698 3.71 5.32 0.999 0.0006 10.7 +#> 7 0.705 3.68 5.21 0.999 0.0007 10.5 +#> 8 0.712 3.64 5.12 0.999 0.0008 10.3 +#> 9 0.717 3.61 5.04 0.999 0.0009 10.1 +#> 10 0.722 3.59 4.97 0.999 0.001 9.97 #> # … with 9,989 more rows
    diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html index d5bb10f..940f91e 100644 --- a/docs/reference/curve_surv.html +++ b/docs/reference/curve_surv.html @@ -87,7 +87,7 @@ concurve - 2.1.0 + 2.2.0 diff --git a/docs/reference/curve_table-1.png b/docs/reference/curve_table-1.png new file mode 100644 index 0000000000000000000000000000000000000000..0fc86320860eb9f7da0f788f8d1debf20a96fd6e GIT binary patch literal 120108 zcmeFZby!qw+dc{iN`q3;AWC=lpa`gRORGppcej8vNW%~U5>iq_cS*-6HS_=j4Barq zUOaohzx}@7`+WcJ8Y zjW(%tld}cW(-BP@!4Q`s5WzEo1RcR|HI4rB^TKw?uZ3ryYIbCkK4<-OZr zv4^$M@Z)WAJSjSPdN~&b|B7>1>=!AHxX)oZ6!*XBs?%~96)Rx~tKzxW8Z63odYjDr znZL;{tCOcCV{oMZP`zG+_azp6zfgP74c+T9o*uNNPj~uZqmECeo5!nu*>tMQS=uk! z%6ye`1^NX`Tl3SG6yn&So^yG86yMKDlN{ww$R8VPxgRoI=W{9@_TJxQ+jdxn9NBgi zj;3Wxf8KjhFDhbR{ZfbTmA}r#oE>vLbC)*&WL{qx*s>UkX}*naPOsIDeS?I z*6jSU+6YEJ#Ao3n&K0gNqaug_&5r4k9|=9+4!7<)A^pQc3C#BJMuYT&J|~o|X4WBM zZA&P9(A)Fn)n&8it5aX6Y_Xk9pN*r>$y^VvzNaJxlXVAw@&6|O{1mZrQ#tpB_#Wja 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z*{TES;ikzoUKw@YUE}}x>Kw2Szvl}_oePo6GXD>^`d^T4$8Ju9i%aiLLzdo^M zt_MKqsTx#f=>Bp59mEP}CwW0c_kVvj1n0&>ByXeXBIe%?2!I&<%uJPkz5vAbnGmeJ zNhqZFmjimi2{Mq&CjWGD@S5}z1nRn`G|K$#fPa_3zf0i%GrNFOc8fj=%I*6~vNHeN zH1h9`@b8ZB??d6=hr+*c!M}0AzoFs3q2a&T!N1wTzX{^M3F5ysf`4lS|CWjVe_bZJ ZkkUpDwI;iDq@aL5N^ + + + + + + + +Produces a Table of Relevant Statistics from the `concurve` functions +<!-- %% ~~function to do ... ~~ --> — curve_table • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    curve_table(data, levels, type = "data.frame")
    + +

    Arguments

    + + + + + + + + + + + + + + +
    data

    levels

    type

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + +(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Lower Limit Upper Limit Limit Ratio Interval Level (%) P-value +#> 2501 0.043 0.083 1.911 25.0 0.750 +#> 5001 0.021 0.105 4.930 50.0 0.500 +#> 7501 -0.008 0.135 -16.319 75.0 0.250 +#> 8001 -0.016 0.143 -8.704 80.0 0.200 +#> 8501 -0.026 0.153 -5.819 85.0 0.150 +#> 9001 -0.039 0.166 -4.241 90.0 0.100 +#> 9501 -0.059 0.185 -3.156 95.0 0.050 +#> 9751 -0.076 0.203 -2.660 97.5 0.025 +#> 9901 -0.097 0.224 -2.303 99.0 0.010 +#> S-value (bits) +#> 2501 0.415 +#> 5001 1.000 +#> 7501 2.000 +#> 8001 2.322 +#> 8501 2.737 +#> 9001 3.322 +#> 9501 4.322 +#> 9751 5.322 +#> 9901 6.644
    (z <- curve_table(intervalsdf, type = "tibble"))
    #> # A tibble: 9 x 1 +#> subdf$`Lower Li… $`Upper Limit` $`Limit Ratio` $`Interval Leve… $`P-value` +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.043 0.083 1.91 25 0.75 +#> 2 0.021 0.105 4.93 50 0.5 +#> 3 -0.008 0.135 -16.3 75 0.25 +#> 4 -0.016 0.143 -8.70 80 0.2 +#> 5 -0.026 0.153 -5.82 85 0.15 +#> 6 -0.039 0.166 -4.24 90 0.1 +#> 7 -0.059 0.185 -3.16 95 0.05 +#> 8 -0.076 0.203 -2.66 97.5 0.025 +#> 9 -0.097 0.224 -2.30 99 0.01 +#> # … with 1 more variable: $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf, type = "latex"))
    #> +#> +#> | | Lower Limit| Upper Limit| Limit Ratio| Interval Level (%)| P-value| S-value (bits)| +#> |:----|-----------:|-----------:|-----------:|------------------:|-------:|--------------:| +#> |2501 | 0.043| 0.083| 1.911| 25.0| 0.750| 0.415| +#> |5001 | 0.021| 0.105| 4.930| 50.0| 0.500| 1.000| +#> |7501 | -0.008| 0.135| -16.319| 75.0| 0.250| 2.000| +#> |8001 | -0.016| 0.143| -8.704| 80.0| 0.200| 2.322| +#> |8501 | -0.026| 0.153| -5.819| 85.0| 0.150| 2.737| +#> |9001 | -0.039| 0.166| -4.241| 90.0| 0.100| 3.322| +#> |9501 | -0.059| 0.185| -3.156| 95.0| 0.050| 4.322| +#> |9751 | -0.076| 0.203| -2.660| 97.5| 0.025| 5.322| +#> |9901 | -0.097| 0.224| -2.303| 99.0| 0.010| 6.644|
    (z <- curve_table(intervalsdf, type = "image"))
    #> a flextable object. +#> col_keys: `Lower Limit`, `Upper Limit`, `Limit Ratio`, `Interval Level (%)`, `P-value`, `S-value (bits)` +#> header has 1 row(s) +#> body has 9 row(s) +#> original dataset sample: +#> Lower Limit Upper Limit Limit Ratio Interval Level (%) P-value +#> 2501 0.043 0.083 1.911 25 0.75 +#> 5001 0.021 0.105 4.930 50 0.50 +#> 7501 -0.008 0.135 -16.319 75 0.25 +#> 8001 -0.016 0.143 -8.704 80 0.20 +#> 8501 -0.026 0.153 -5.819 85 0.15 +#> S-value (bits) +#> 2501 0.415 +#> 5001 1.000 +#> 7501 2.000 +#> 8001 2.322 +#> 8501 2.737
    +
    +
    + +
    + + + +
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    R

    Examples

    # Simulate random data -GroupA <- rnorm(50) -GroupB <- rnorm(50) +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -RandomModel <- lm(GroupA ~ GroupB, data = RandomData) -intervalsdf <- curve_gen(RandomModel, "GroupB") -ggconcurve(type = "consonance", data = intervalsdf, nullvalue = "present")
    -ggconcurve(type = "surprisal", data = intervalsdf)
    +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values")))
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    +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
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    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
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    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) + +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
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    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) + +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level."))
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    @@ -176,6 +176,13 @@

    curve_lik()

    + +

    Computes Likelihood Intervals for Ratio Data +

    + +

    curve_mean()

    @@ -200,12 +207,26 @@

    curve_table()

    + +

    Produces a Table of Relevant Statistics from the `concurve` functions +

    + +

    ggconcurve()

    Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2

    + +

    plot_compare()

    + +

    Compares Different Functions on the Same Plot +

    + +

    plot_concurve()

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z2X;GW&NLJ`kOu=&9dyNuKFAt;=JINC&w~54hRzS?Qe8knOa)M@HA93g%Oqj08C)^V zdxhSiM|5FoKFGW%yryyM_@q)HbsFu@kQIwAhuDSbuUfDJKUn^*GyfVzLXP^ma$8R7 zn&v&|jSs+)q|hI8W=8MX8MT%~o=`H3mfB!ps+gtRrG7ai5-d*cMc?6U-DDF5(^2&;RnP0{^D`xTI|F-EE(0iQ)OL@gBfI2(lU$S*JZNCAEj zUd3p|~kC;$O*+5D_RE^U3B;lclv3!e3WjzP* z6aMctO9EiZVUff8j6~jK@yyDDB1A;FFWW_iPGm}g2TY2TN24tL$n{RC>XYKWruoJL xdFBN_(EEM0{dIqh{###u@#X)JFXIfwA6G}3zV concurve - 2.1.0 + 2.2.0 diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 79e4879..e0fc75e 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -9,6 +9,9 @@ https://data.lesslikely.com/concurve//reference/curve_gen.html + + https://data.lesslikely.com/concurve//reference/curve_lik.html + https://data.lesslikely.com/concurve//reference/curve_mean.html @@ -21,12 +24,18 @@ https://data.lesslikely.com/concurve//reference/curve_surv.html + + https://data.lesslikely.com/concurve//reference/curve_table.html + https://data.lesslikely.com/concurve//reference/defunct.html https://data.lesslikely.com/concurve//reference/ggconcurve.html + + https://data.lesslikely.com/concurve//reference/plot_compare.html + https://data.lesslikely.com/concurve//reference/plot_concurve.html diff --git a/inst/CITATION b/inst/CITATION index a6e1bd1..b895d4a 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -3,10 +3,9 @@ note <- sprintf("R package version %s", meta$Version) bibentry( bibtype = "Manual", - title = paste("{concurve}: Computes and Plots Consonance (Confidence) Intervals, P-Values, and S-Values to Form Consonance and Surprisal Functions"), + title = paste("{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions"), author = c(person("Zad R.", "Chow"), person("Andrew D.", "Vigotsky")), year = year, note = note, - url = "https://CRAN.R-project.org/package=concurve", - doi = "10.5281/zenodo.1308151" + url = "https://CRAN.R-project.org/package=concurve" ) diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd new file mode 100644 index 0000000..3fb640e --- /dev/null +++ b/man/curve_lik.Rd @@ -0,0 +1,76 @@ +\name{curve_lik} +\alias{curve_lik} +%- Also NEED an '\alias' for EACH other topic documented here. +\title{Computes Likelihood Intervals for Ratio Data +%% ~~function to do ... ~~ +} +\description{ +%% ~~ A concise (1-5 lines) description of what the function does. ~~ +} +\usage{ +curve_lik(point, LL, UL, measure = "ratio", steps = 10000) +} +%- maybe also 'usage' for other objects documented here. +\arguments{ + \item{point}{ +%% ~~Describe \code{point} here~~ +} + \item{LL}{ +%% ~~Describe \code{LL} here~~ +} + \item{UL}{ +%% ~~Describe \code{UL} here~~ +} + \item{measure}{ +%% ~~Describe \code{measure} here~~ +} + \item{steps}{ +%% ~~Describe \code{steps} here~~ +} +} +\details{ +%% ~~ If necessary, more details than the description above ~~ +} +\value{ +%% ~Describe the value returned +%% If it is a LIST, use +%% \item{comp1 }{Description of 'comp1'} +%% \item{comp2 }{Description of 'comp2'} +%% ... +} +\references{ +%% ~put references to the literature/web site here ~ +} +\author{ +%% ~~who you are~~ +} +\note{ +%% ~~further notes~~ +} + +%% ~Make other sections like Warning with \section{Warning }{....} ~ + +\seealso{ +%% ~~objects to See Also as \code{\link{help}}, ~~~ +} +\examples{ + +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) + +tibble::tibble(lik1) + +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) + +tibble::tibble(lik2) + +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present")) + +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present")) + +} +% Add one or more standard keywords, see file 'KEYWORDS' in the +% R documentation directory. +\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") +\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/curve_table.Rd b/man/curve_table.Rd new file mode 100644 index 0000000..228ff26 --- /dev/null +++ b/man/curve_table.Rd @@ -0,0 +1,73 @@ +\name{curve_table} +\alias{curve_table} +%- Also NEED an '\alias' for EACH other topic documented here. +\title{Produces a Table of Relevant Statistics from the `concurve` functions +%% ~~function to do ... ~~ +} +\description{ +%% ~~ A concise (1-5 lines) description of what the function does. ~~ +} +\usage{ +curve_table(data, levels, type = "data.frame") +} +%- maybe also 'usage' for other objects documented here. +\arguments{ + \item{data}{ +%% ~~Describe \code{data} here~~ +} + \item{levels}{ +%% ~~Describe \code{levels} here~~ +} + \item{type}{ +%% ~~Describe \code{type} here~~ +} +} +\details{ +%% ~~ If necessary, more details than the description above ~~ +} +\value{ +%% ~Describe the value returned +%% If it is a LIST, use +%% \item{comp1 }{Description of 'comp1'} +%% \item{comp2 }{Description of 'comp2'} +%% ... +} +\references{ +%% ~put references to the literature/web site here ~ +} +\author{ +%% ~~who you are~~ +} +\note{ +%% ~~further notes~~ +} + +%% ~Make other sections like Warning with \section{Warning }{....} ~ + +\seealso{ +%% ~~objects to See Also as \code{\link{help}}, ~~~ +} +\examples{ + +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + +(z <- curve_table(intervalsdf, type = "data.frame")) +(z <- curve_table(intervalsdf, type = "tibble")) +(z <- curve_table(intervalsdf, type = "latex")) +(z <- curve_table(intervalsdf, type = "image")) + +} +% Add one or more standard keywords, see file 'KEYWORDS' in the +% R documentation directory. +\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") +\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index e6f5ce1..9b0717a 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -66,16 +66,41 @@ Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. \examples{ # Simulate random data -GroupA <- rnorm(50) -GroupB <- rnorm(50) +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -RandomModel <- lm(GroupA ~ GroupB, data = RandomData) -intervalsdf <- curve_gen(RandomModel, "GroupB") -ggconcurve(type = "consonance", data = intervalsdf, nullvalue = "present") +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values"))) + +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level.")) + +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level.")) + +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) + +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level.")) + +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -ggconcurve(type = "surprisal", data = intervalsdf) +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level.")) } diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd new file mode 100644 index 0000000..b33117f --- /dev/null +++ b/man/plot_compare.Rd @@ -0,0 +1,130 @@ +\name{plot_compare} +\alias{plot_compare} +%- Also NEED an '\alias' for EACH other topic documented here. +\title{Compares Different Functions on the Same Plot +%% ~~function to do ... ~~ +} +\description{ +%% ~~ A concise (1-5 lines) description of what the function does. ~~ +} +\usage{ +plot_compare(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", xaxis = "Θ Range of Values", yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b") +} +%- maybe also 'usage' for other objects documented here. +\arguments{ + \item{data1}{ +%% ~~Describe \code{data1} here~~ +} + \item{data2}{ +%% ~~Describe \code{data2} here~~ +} + \item{type}{ +%% ~~Describe \code{type} here~~ +} + \item{measure}{ +%% ~~Describe \code{measure} here~~ +} + \item{nullvalue}{ +%% ~~Describe \code{nullvalue} here~~ +} + \item{position}{ +%% ~~Describe \code{position} here~~ +} + \item{title}{ +%% ~~Describe \code{title} here~~ +} + \item{subtitle}{ +%% ~~Describe \code{subtitle} here~~ +} + \item{xaxis}{ +%% ~~Describe \code{xaxis} here~~ +} + \item{yaxis}{ +%% ~~Describe \code{yaxis} here~~ +} + \item{color}{ +%% ~~Describe \code{color} here~~ +} + \item{fill1}{ +%% ~~Describe \code{fill1} here~~ +} + \item{fill2}{ +%% ~~Describe \code{fill2} here~~ +} +} +\details{ +%% ~~ If necessary, more details than the description above ~~ +} +\value{ +%% ~Describe the value returned +%% If it is a LIST, use +%% \item{comp1 }{Description of 'comp1'} +%% \item{comp2 }{Description of 'comp2'} +%% ... +} +\references{ +%% ~put references to the literature/web site here ~ +} +\author{ +%% ~~who you are~~ +} +\note{ +%% ~~further notes~~ +} + +%% ~Make other sections like Warning with \section{Warning }{....} ~ + +\seealso{ +%% ~~objects to See Also as \code{\link{help}}, ~~~ +} +\examples{ +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values"))) + +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level.")) + +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level.")) + +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level.")) + +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level.")) + +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level.")) + +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level.")) + + +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio")) + +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio")) + + +} +% Add one or more standard keywords, see file 'KEYWORDS' in the +% R documentation directory. +\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") +\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line From 764d548c47d1d7deead45443b07e6617b469ae53 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Mon, 25 Nov 2019 04:17:38 -0500 Subject: [PATCH 04/21] curve_compare --- R/curve_compare.R | 134 +++++++++++++++ R/curve_corr.R | 10 +- R/curve_gen.R | 12 +- R/curve_lik.R | 8 +- R/curve_mean.R | 8 +- R/curve_meta.R | 6 +- R/curve_rev.R | 12 +- R/curve_surv.R | 6 +- R/curve_table.R | 16 +- R/defunct.R | 5 +- R/ggconcurve.R | 8 +- R/plot_compare.R | 162 +++++++++++++----- R/plot_concurve.R | 92 +++++----- README.Rmd | 2 +- README.md | 2 +- codemeta.json | 2 +- docs/index.html | 2 +- docs/reference/curve_compare.html | 260 +++++++++++++++++++++++++++++ docs/reference/curve_corr.html | 24 +-- docs/reference/curve_gen.html | 24 +-- docs/reference/curve_lik.html | 4 +- docs/reference/curve_mean.html | 24 +-- docs/reference/curve_meta.html | 24 +-- docs/reference/curve_rev.html | 24 +-- docs/reference/curve_table.html | 78 ++++----- docs/reference/ggconcurve-1.png | Bin 154191 -> 153332 bytes docs/reference/ggconcurve-2.png | Bin 153029 -> 152492 bytes docs/reference/ggconcurve.html | 10 +- docs/reference/index.html | 6 + docs/reference/plot_compare-1.png | Bin 155667 -> 154928 bytes docs/reference/plot_compare-2.png | Bin 151401 -> 150974 bytes docs/reference/plot_compare-4.png | Bin 177292 -> 183267 bytes docs/reference/plot_compare-5.png | Bin 186938 -> 191728 bytes docs/reference/plot_compare-8.png | Bin 145632 -> 145043 bytes docs/reference/plot_compare-9.png | Bin 139267 -> 140867 bytes docs/reference/plot_compare.html | 18 +- docs/reference/plot_concurve-1.png | Bin 113799 -> 116871 bytes docs/sitemap.xml | 3 + man/curve_compare.Rd | 61 +++++++ man/ggconcurve.Rd | 2 +- man/plot_compare.Rd | 6 +- 41 files changed, 795 insertions(+), 260 deletions(-) create mode 100644 R/curve_compare.R create mode 100644 docs/reference/curve_compare.html create mode 100644 man/curve_compare.Rd diff --git a/R/curve_compare.R b/R/curve_compare.R new file mode 100644 index 0000000..2012925 --- /dev/null +++ b/R/curve_compare.R @@ -0,0 +1,134 @@ +curve_compare <- function(data1, data2, type = "consonance", plot = TRUE, ...) { + + # Consonance Function ----------------------------------------------------- + + if (type == "consonance") { + if (is.data.frame(data1) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data1) != 6) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (is.data.frame(data2) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data2) != 6) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (plot == TRUE) { + plot_comparison <- (plot_compare(data1, data2, type = "consonance", ...)) + } else if (plot == FALSE) { + + } + + df1 <- pivot_longer(data1, lower.limit:upper.limit, names_to = "limit.bound", values_to = "Limit") + + df2 <- pivot_longer(data2, lower.limit:upper.limit, names_to = "limit.bound", values_to = "Limit") + + df1 <- data.frame( + "x" = df1$Limit, + "y" = df1$pvalue + ) + df2 <- data.frame( + "x" = df2$Limit, + "y" = df2$pvalue + ) + + if (max(df1$x) < min(df2$x) || min(df1$x) > max(df2$x)) { + print("Out of Range, AUC = 0") + } else { + f0 <- approxfun(df1$x, df1$y, ties = "mean") + f1 <- approxfun(df2$x, df2$y, ties = "mean") + f <- Vectorize(function(x) { + min(f0(x), f1(x)) + }) + domain <- c( + max(min(df1$x), min(df2$x)), + min(max(df1$x), max(df2$x)) + ) + AUC_1 <- integrate(f0, min(df1$x), max(df1$x))$value + AUC_2 <- integrate(f1, min(df2$x), max(df2$x))$value + AUC_shared <- integrate(f, domain[1], domain[2])$value + + AUC_overlap <- (AUC_shared / (AUC_1 + AUC_2 - AUC_shared)) + AUC_ratio <- (AUC_shared / (AUC_1 + AUC_2 - 2 * AUC_shared)) + + AUC_results <- data.frame(AUC_1, AUC_2, AUC_shared, AUC_overlap, AUC_ratio) + AUC_results <- round(AUC_results, digits = 3) + names <- c("AUC 1", "AUC 2", "Shared AUC", "AUC Overlap (%)", "Overlap:Non-Overlap AUC Ratio") + colnames(AUC_results) <- names + AUC_results <- knitr::kable( + AUC_results, + booktabs = TRUE + ) + print("AUC = Area Under the Curve") + return(list(AUC_results, plot_comparison)) + } + + + # Surprisal Function ------------------------------------------------------ + } else if (type == "surprisal") { + if (is.data.frame(data1) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data1) != 6) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (is.data.frame(data2) != TRUE) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + if (ncol(data2) != 6) { + stop("Error: 'x' must be a data frame from 'concurve'.") + } + + if (plot == TRUE) { + plot_comparison <- (plot_compare(data1, data2, type = "surprisal", ...)) + } else if (plot == FALSE) { + + } + + df1 <- pivot_longer(data1, lower.limit:upper.limit, names_to = "limit.bound", values_to = "Limit") + + df2 <- pivot_longer(data2, lower.limit:upper.limit, names_to = "limit.bound", values_to = "Limit") + + df1 <- data.frame( + "x" = df1$Limit, + "y" = max(df1$svalue) - df1$svalue + ) + df2 <- data.frame( + "x" = df2$Limit, + "y" = max(df2$svalue) - df2$svalue + ) + + if (max(df1$x) < min(df2$x) || min(df1$x) > max(df2$x)) { + print("Out of Range, AUC = 0") + } else { + f0 <- approxfun(df1$x, df1$y, ties = "mean") + f1 <- approxfun(df2$x, df2$y, ties = "mean") + f <- Vectorize(function(x) { + min(f0(x), f1(x)) + }) + domain <- c( + max(min(df1$x), min(df2$x)), + min(max(df1$x), max(df2$x)) + ) + AUC_1 <- integrate(f0, min(df1$x), max(df1$x))$value + AUC_2 <- integrate(f1, min(df2$x), max(df2$x))$value + AUC_shared <- integrate(f, domain[1], domain[2])$value + + AUC_overlap <- (AUC_shared / (AUC_1 + AUC_2 - AUC_shared)) + AUC_ratio <- (AUC_shared / (AUC_1 + AUC_2 - 2 * AUC_shared)) + + AUC_results <- data.frame(AUC_1, AUC_2, AUC_shared, AUC_overlap, AUC_ratio) + AUC_results <- round(AUC_results, digits = 3) + names <- c("AUC 1", "AUC 2", "Shared AUC", "AUC Overlap (%)", "Overlap:Non-Overlap AUC Ratio") + colnames(AUC_results) <- names + AUC_results <- knitr::kable( + AUC_results, + booktabs = TRUE + ) + print("AUC = Area Under the Curve") + return(list(AUC_results, plot_comparison)) + } + } +} diff --git a/R/curve_corr.R b/R/curve_corr.R index fa46ecd..bd01cc9 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -10,14 +10,16 @@ curve_corr <- function(x, y, alternative, method, steps = 10000) { } intrvls <- (0:steps) / steps - results <- mclapply(intrvls, FUN = function(i) cor.test(x, y, + results <- mclapply(intrvls, FUN = function(i) { + cor.test(x, y, alternative = alternative, method = method, exact = NULL, conf.level = i, continuity = FALSE - )$conf.int[]) + )$conf.int[] + }, mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) @@ -26,4 +28,4 @@ curve_corr <- function(x, y, alternative, method, steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_gen.R b/R/curve_gen.R index 2e2c7ea..98c142a 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -15,11 +15,11 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = } intrvls <- (0:steps) / steps if (method == "default") { - results <- mclapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ]) + results <- mclapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) } else if (method == "Wald") { - results <- mclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ]) + results <- mclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) } else if (method == "lm") { - results <- mclapply(intrvls, FUN = function(i) confint.lm(object = model, level = i)[var, ]) + results <- mclapply(intrvls, FUN = function(i) confint.lm(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) } else if (method == "boot") { effect <- coef(model)[[var]] boot_dist <- replicate(replicates, @@ -27,13 +27,13 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = data = model$model[sample(nrow(model$model), replace = T), ] ))[[var]] ) - effect - results <- mclapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2)) + results <- mclapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores(logical = FALSE) - 1) } df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) @@ -42,4 +42,4 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_lik.R b/R/curve_lik.R index f8be462..3391fb6 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -17,8 +17,8 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- mclapply(z, FUN = function(i) point + (i * se)) - UL <- mclapply(z, FUN = function(i) point - (i * se)) + LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores(logical = FALSE) - 1) + UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -27,8 +27,8 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- mclapply(z, FUN = function(i) logpoint + (i * se)) - logUL <- mclapply(z, FUN = function(i) logpoint - (i * se)) + logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores(logical = FALSE) - 1) + logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_mean.R b/R/curve_mean.R index 378dd21..772d462 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -18,7 +18,7 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = } intrvls <- (0:steps) / steps if (method == "default") { - results <- mclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[]) + results <- mclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], mc.cores = detectCores(logical = FALSE) - 1) } else if (method == "boot") { diff <- mean(x) - mean(y) if (paired) { @@ -32,12 +32,12 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = mean(sample(y, length(y), replace = T)) ) - diff } - results <- mclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2)) + results <- mclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores(logical = FALSE) - 1) } df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) @@ -47,4 +47,4 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_meta.R b/R/curve_meta.R index 98533c7..2b23e1e 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -11,7 +11,7 @@ curve_meta <- function(x, measure = "default", steps = 10000) { stop("Error: 'steps' must be a numeric vector") } intrvls <- (0:steps) / steps - results <- mclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[]) + results <- mclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -25,10 +25,10 @@ curve_meta <- function(x, measure = "default", steps = 10000) { df$lower.limit <- exp(df$lower.limit) df$upper.limit <- exp(df$upper.limit) } - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df <- head(df, -1) return(df) } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_rev.R b/R/curve_rev.R index a4e9e21..a997758 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -19,8 +19,8 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- mclapply(z, FUN = function(i) point + (i * se)) - UL <- mclapply(z, FUN = function(i) point - (i * se)) + LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores(logical = FALSE) - 1) + UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -29,15 +29,15 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- mclapply(z, FUN = function(i) logpoint + (i * se)) - logUL <- mclapply(z, FUN = function(i) logpoint - (i * se)) + logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores(logical = FALSE) - 1) + logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit df$lower.limit <- exp(df$lower.limit) df$upper.limit <- exp(df$upper.limit) } - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- 1 - intrvls df$pvalue <- 1 - (1 - intrvls) df$svalue <- -log2(df$pvalue) @@ -46,4 +46,4 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_surv.R b/R/curve_surv.R index 4bb772c..4e86dc0 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -9,12 +9,12 @@ curve_surv <- function(data, x, steps = 10000) { } intrvls <- (1:steps) / steps - results <- mclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ]) + results <- mclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], mc.cores = detectCores(logical = FALSE) - 1) df <- data.frame(do.call(rbind, results))[, 3:4] intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit - df$limit.ratio <- (df$upper.limit) / (df$lower.limit) + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) @@ -23,4 +23,4 @@ curve_surv <- function(data, x, steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/curve_table.R b/R/curve_table.R index 396fc48..b9040a5 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,45 +1,35 @@ curve_table <- function(data, levels, type = "data.frame") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - subdf <- mclapply(levels, FUN = function(i) (subset(data, intrvl.level == i))) + subdf <- mclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores(logical = FALSE) - 1) subdf <- data.frame(do.call(rbind, subdf)) subdf$intrvl.level <- (subdf$intrvl.level * 100) - subcolnames <- c("Lower Limit", "Upper Limit", "Limit Ratio", "Interval Level (%)", "P-value", "S-value (bits)") + subcolnames <- c("Lower Limit", "Upper Limit", "Interval Width", "Interval Level (%)", "P-value", "S-value (bits)") colnames(subdf) <- subcolnames subdf <- round(subdf, digits = 3) if (type == "data.frame") { - return(subdf) - } else if (type == "tibble") { - subdf <- tibble::tibble(subdf) return(subdf) - } else if (type == "docx") { - subdf <- flextable(subdf) subdf <- autofit(subdf) subdf return(print(subdf, preview = "docx")) - } else if (type == "pptx") { - subdf <- flextable(subdf) subdf <- autofit(subdf) subdf return(print(subdf, preview = "pptx")) - } else if (type == "latex") { - subdf <- knitr::kable( subdf, booktabs = TRUE, label = "A table of some interval estimates at various levels and corresponding statistics." ) return(subdf) - } else if (type == "image") { subdf <- flextable(subdf) subdf <- autofit(subdf) @@ -48,4 +38,4 @@ curve_table <- function(data, levels, type = "data.frame") { } } -utils::globalVariables(c("subdf", "Lower Limit", "Limit Ratio", "Upper Limit", "Interval Level", "P-value", "S-value")) +utils::globalVariables(c("subdf", "Lower Limit", "Upper Limit", "Interval Width", "Interval Level", "P-value", "S-value")) diff --git a/R/defunct.R b/R/defunct.R index c7079c0..2d15220 100644 --- a/R/defunct.R +++ b/R/defunct.R @@ -1,4 +1,6 @@ -defunct <- function(msg = "This function is deprecated") function(...) return(stop(msg)) +defunct <- function(msg = "This function is deprecated") function(...) { + return(stop(msg)) +} # Graphical functions plotpint <- defunct("plotpint() is now deprecated. Please use ggconcurve() or plot_concurve() instead.") @@ -11,4 +13,3 @@ genintervals <- defunct("genintervals() is now deprecated. Please use curve_gen( corrintervals <- defunct("corrintervals() is now deprecated. Please use curve_corr() instead.") survintervals <- defunct("survintervals() is now deprecated. Please use curve_surv() instead.") rev_eng <- defunct("rev_eng() is now deprecated. Please use curve_rev instead.") - diff --git a/R/ggconcurve.R b/R/ggconcurve.R index fb1d3ae..1a0b20c 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -1,7 +1,7 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = 0.95, nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", - xaxis = "\u0398 Range of Values", + xaxis = expression(Theta~"Range of Values"), yaxis = "P-value", color = "#555555", fill = "#239a98") { @@ -41,9 +41,9 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = # Plotting Intervals ------------------------------------------------------ - interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2]))) + interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores(logical = FALSE) - 1) interval <- data.frame(do.call(rbind, interval)) - interval <- gather(interval, key = "levels", value = "limits", X2:X3) + interval <- pivot_longer(interval, X2:X3, names_to = "levels", values_to = "limits") interval <- interval[, -2] colum_names <- c("levels", "limits") colnames(interval) <- colum_names @@ -146,7 +146,7 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = # Plotting Intervals ------------------------------------------------------ - interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2]))) + interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores(logical = FALSE) - 1) interval <- data.frame(do.call(rbind, interval)) interval <- gather(interval, key = "levels", value = "limits", X2:X3) interval <- interval[, -2] diff --git a/R/plot_compare.R b/R/plot_compare.R index 7b98114..5d8a75e 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -1,14 +1,15 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Functions", subtitle = "The function displays intervals at every level.", - xaxis = "\u0398 Range of Values", + xaxis = expression(Theta~"Range of Values"), yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b") { - + cols <- c(fill1, fill2) # Consonance Function ----------------------------------------------------- + if (type == "consonance") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") @@ -51,40 +52,39 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", } ggplot(data = data1) + geom_point(aes(x = lower.limit, y = pvalue), - color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + color = color, fill = fill1, alpha = 0.5, size = 0.1 ) + geom_point(aes(x = upper.limit, y = pvalue), - color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + color = color, fill = fill1, alpha = 0.5, size = 0.1 ) + - geom_ribbon(aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue), - fill = fill1, alpha = 0.30 + geom_ribbon(aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue, fill = fill1), + alpha = 0.30 ) + - geom_ribbon(aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue), - fill = fill1, alpha = 0.30 + geom_ribbon(aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue, fill = fill1), + alpha = 0.30 ) + geom_point( data = data2, aes(x = lower.limit, y = pvalue), - color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + color = color, fill = fill2, alpha = 0.5, size = 0.1 ) + geom_point( data = data2, aes(x = upper.limit, y = pvalue), - color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + color = color, fill = fill2, alpha = 0.5, size = 0.1 ) + geom_ribbon( - data = data2, aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue), - fill = fill2, alpha = 0.30 + data = data2, aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue, fill = fill2), + alpha = 0.30 ) + geom_ribbon( - data = data2, aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue), - fill = fill2, alpha = 0.30 + data = data2, aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue, fill = fill2), + alpha = 0.30 ) + theme_bw() + labs( title = title, subtitle = subtitle, x = xaxis, - y = yaxis, - color = fill1 + y = yaxis ) + theme( plot.title = element_text(size = 12), @@ -92,8 +92,27 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", axis.title.x = element_text(size = 12), axis.title.y = element_text(size = 12), text = element_text(size = 11), - legend.position = "bottom" + legend.background = element_blank(), + legend.position = c(.998, .998), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + { if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) } + @@ -175,11 +194,11 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", geom_point(aes(x = upper.limit, y = svalue), color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 ) + - geom_ribbon(aes(x = lower.limit, ymin = max(svalue), ymax = svalue), - fill = fill1, alpha = 0.30 + geom_ribbon(aes(x = lower.limit, ymin = max(svalue), ymax = svalue, fill = fill1), + alpha = 0.30 ) + - geom_ribbon(aes(x = upper.limit, ymin = max(svalue), ymax = svalue), - fill = fill1, alpha = 0.30 + geom_ribbon(aes(x = upper.limit, ymin = max(svalue), ymax = svalue, fill = fill1), + alpha = 0.30 ) + geom_point( data = data2, aes(x = lower.limit, y = svalue), @@ -190,11 +209,11 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 ) + geom_ribbon( - data = data2, aes(x = lower.limit, ymin = max(svalue), ymax = svalue), - fill = fill2, alpha = 0.30 + data = data2, aes(x = lower.limit, ymin = max(svalue), ymax = svalue, fill = fill2), + alpha = 0.30 ) + geom_ribbon( - data = data2, aes(x = upper.limit, ymin = max(svalue), ymax = svalue), + data = data2, aes(x = upper.limit, ymin = max(svalue), ymax = svalue, fill = fill2), fill = fill2, alpha = 0.30 ) + labs( @@ -209,8 +228,28 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", plot.subtitle = element_text(size = 11), axis.title.x = element_text(size = 12), axis.title.y = element_text(size = 12), - text = element_text(size = 11) + text = element_text(size = 11), + legend.background = element_blank(), + legend.position = c(.998, .2), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + { if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) } + @@ -255,9 +294,9 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ggplot(data = data1, mapping = aes(x = values, y = support)) + geom_line() + - geom_ribbon(aes(x = values, ymin = min(support), ymax = support), fill = fill1, alpha = 0.30) + + geom_ribbon(aes(x = values, ymin = min(support), ymax = support, fill = fill1), alpha = 0.30) + geom_line(data = data2) + - geom_ribbon(data = data2, aes(x = values, ymin = min(support), ymax = support), fill = fill2, alpha = 0.30) + + geom_ribbon(data = data2, aes(x = values, ymin = min(support), ymax = support, fill = fill2), alpha = 0.30) + labs( title = "Likelihood Functions", subtitle = subtitle, @@ -266,13 +305,32 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ) + theme_bw() + theme( - plot.title = element_text(size = 14), - plot.subtitle = element_text(size = 12), - plot.caption = element_text(size = 8), - axis.title.x = element_text(size = 13), - axis.title.y = element_text(size = 13), - text = element_text(size = 15) + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11), + legend.background = element_blank(), + legend.position = c(.998, .998), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + @@ -290,6 +348,7 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ) } } + # Deviance Function ----------------------------------------------------- } else if (type == "deviance") { if (ncol(data1) != 4) { @@ -325,9 +384,9 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ggplot(data = data1, mapping = aes(x = values, y = deviancestat)) + geom_line() + - geom_ribbon(aes(x = values, ymin = deviancestat, ymax = max(deviancestat)), fill = fill1, alpha = 0.30) + + geom_ribbon(aes(x = values, ymin = deviancestat, ymax = max(deviancestat), fill = fill1), alpha = 0.30) + geom_line(data = data2) + - geom_ribbon(data = data2, aes(x = values, ymin = deviancestat, ymax = max(deviancestat)), fill = fill2, alpha = 0.30) + + geom_ribbon(data = data2, aes(x = values, ymin = deviancestat, ymax = max(deviancestat), fill = fill2), alpha = 0.30) + labs( title = "Deviance Functions", subtitle = subtitle, @@ -336,13 +395,32 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ) + theme_bw() + theme( - plot.title = element_text(size = 14), - plot.subtitle = element_text(size = 12), - plot.caption = element_text(size = 8), - axis.title.x = element_text(size = 13), - axis.title.y = element_text(size = 13), - text = element_text(size = 15) + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11), + legend.background = element_blank(), + legend.position = c(.998, .2), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + @@ -365,4 +443,4 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/R/plot_concurve.R b/R/plot_concurve.R index b86598f..4c3b8f2 100644 --- a/R/plot_concurve.R +++ b/R/plot_concurve.R @@ -3,7 +3,7 @@ plot_concurve <- function(data, measure = "default", intervals = FALSE, title = "Consonance Function", - xlab = "Theta", + xlab = expression(Theta~"Range of Values"), ylab1 = "P-value", ylab2 = "Confidence Level (%)", fontsize = 12, @@ -104,50 +104,50 @@ plot_concurve <- function(data, # Labels for interval estimates and maximum likelihood if (intervals == TRUE) { - text( - x = 1.27, y = 3, - paste( - "Point Estimate:", - round(((max(data$lower.limit) + min(data$upper.limit)) / 2), 3) - ), - cex = 0.93, col = "black" - ) - text( - x = 1.27, y = 10, - paste("50% CI:", - round(unname((quantile(data$lower.limit, prob = 0.50))), 3), "-", - round(unname((quantile(data$upper.limit, prob = 0.50))), 3), - sep = " ", collapse = ", " - ), - cex = 0.93, col = "black" - ) - text( - x = 1.27, y = 17, - paste("75% CI:", - round(unname((quantile(data$lower.limit, prob = 0.25))), 3), "-", - round(unname((quantile(data$upper.limit, prob = 0.75))), 3), - sep = " ", collapse = ", " - ), - cex = 0.93, col = "black" - ) - text( - x = 1.27, y = 24, - paste("95% CI:", - round(unname((quantile(data$lower.limit, prob = 0.05))), 3), "-", - round(unname((quantile(data$upper.limit, prob = 0.95))), 3), - sep = " ", collapse = ", " - ), - cex = 0.93, col = "black" - ) - text( - x = 1.27, y = 31, - paste("99% CI:", - round(unname((quantile(data$lower.limit, prob = 0.01))), 3), "-", - round(unname((quantile(data$upper.limit, prob = 0.99))), 3), - sep = " ", collapse = ", " - ), - cex = 0.93, col = "black" - ) + text( + x = 1.27, y = 3, + paste( + "Point Estimate:", + round(((max(data$lower.limit) + min(data$upper.limit)) / 2), 3) + ), + cex = 0.93, col = "black" + ) + text( + x = 1.27, y = 10, + paste("50% CI:", + round(unname((quantile(data$lower.limit, prob = 0.50))), 3), "-", + round(unname((quantile(data$upper.limit, prob = 0.50))), 3), + sep = " ", collapse = ", " + ), + cex = 0.93, col = "black" + ) + text( + x = 1.27, y = 17, + paste("75% CI:", + round(unname((quantile(data$lower.limit, prob = 0.25))), 3), "-", + round(unname((quantile(data$upper.limit, prob = 0.75))), 3), + sep = " ", collapse = ", " + ), + cex = 0.93, col = "black" + ) + text( + x = 1.27, y = 24, + paste("95% CI:", + round(unname((quantile(data$lower.limit, prob = 0.05))), 3), "-", + round(unname((quantile(data$upper.limit, prob = 0.95))), 3), + sep = " ", collapse = ", " + ), + cex = 0.93, col = "black" + ) + text( + x = 1.27, y = 31, + paste("99% CI:", + round(unname((quantile(data$lower.limit, prob = 0.01))), 3), "-", + round(unname((quantile(data$upper.limit, prob = 0.99))), 3), + sep = " ", collapse = ", " + ), + cex = 0.93, col = "black" + ) } } @@ -201,4 +201,4 @@ plot_concurve <- function(data, # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) diff --git a/README.Rmd b/README.Rmd index b32f709..93b940d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -40,7 +40,7 @@ concurve | Graph Interval Functions + # Installation diff --git a/README.md b/README.md index 5eec083..2d9469e 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org ## Export Tabled Results Easily For Word, Powerpoint, & LaTeX - + # Installation diff --git a/codemeta.json b/codemeta.json index d98fca4..35e434c 100644 --- a/codemeta.json +++ b/codemeta.json @@ -220,7 +220,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3839.362KB", + "fileSize": "3840.811KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/docs/index.html b/docs/index.html index 9abf8b5..9e74e9b 100644 --- a/docs/index.html +++ b/docs/index.html @@ -131,7 +131,7 @@

    Export Tabled Results Easily For Word, Powerpoint, & LaTeX

    -

    +

    diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html new file mode 100644 index 0000000..bef7a09 --- /dev/null +++ b/docs/reference/curve_compare.html @@ -0,0 +1,260 @@ + + + + + + + + +Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets — curve_compare • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    curve_compare(data1, data2, type = "consonance", plot = TRUE, ...)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    data1

    data2

    type

    plot

    ...

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html index d018981..a8d68ef 100644 --- a/docs/reference/curve_corr.html +++ b/docs/reference/curve_corr.html @@ -201,18 +201,18 @@

    Examp alternative = "two.sided", method = "pearson") tibble::tibble(joe)

    #> # A tibble: 10,000 x 1 -#> joe$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.398 0.398 1 0 1 0 -#> 2 0.398 0.398 1.00 0.0001 1.000 0.000144 -#> 3 0.398 0.398 1.00 0.0002 1.000 0.000289 -#> 4 0.398 0.398 1.00 0.000300 1.000 0.000433 -#> 5 0.398 0.398 1.00 0.0004 1.000 0.000577 -#> 6 0.398 0.398 1.00 0.0005 1.000 0.000722 -#> 7 0.398 0.398 1.00 0.000600 0.999 0.000866 -#> 8 0.398 0.398 1.00 0.0007 0.999 0.00101 -#> 9 0.398 0.398 1.00 0.0008 0.999 0.00115 -#> 10 0.398 0.398 1.00 0.0009 0.999 0.00130 +#> joe$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.398 0.398 0 0 1 0 +#> 2 0.398 0.398 0.0000308 0.0001 1.000 0.000144 +#> 3 0.398 0.398 0.0000616 0.0002 1.000 0.000289 +#> 4 0.398 0.398 0.0000923 0.000300 1.000 0.000433 +#> 5 0.398 0.398 0.000123 0.0004 1.000 0.000577 +#> 6 0.398 0.398 0.000154 0.0005 1.000 0.000722 +#> 7 0.398 0.398 0.000185 0.000600 0.999 0.000866 +#> 8 0.398 0.398 0.000215 0.0007 0.999 0.00101 +#> 9 0.398 0.398 0.000246 0.0008 0.999 0.00115 +#> 10 0.398 0.398 0.000277 0.0009 0.999 0.00130 #> # … with 9,990 more rows
    diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html index 3b64899..da8608d 100644 --- a/docs/reference/curve_gen.html +++ b/docs/reference/curve_gen.html @@ -210,18 +210,18 @@

    Examp bob <- curve_gen(rob, "GroupB", method = "lm") tibble::tibble(bob)
    #> # A tibble: 10,000 x 1 -#> bob$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.0732 -0.0732 1 0 1 0 -#> 2 -0.0732 -0.0731 1.000 0.0001 1.000 0.000144 -#> 3 -0.0732 -0.0731 0.999 0.0002 1.000 0.000289 -#> 4 -0.0732 -0.0731 0.999 0.000300 1.000 0.000433 -#> 5 -0.0732 -0.0731 0.998 0.0004 1.000 0.000577 -#> 6 -0.0732 -0.0731 0.998 0.0005 1.000 0.000722 -#> 7 -0.0733 -0.0731 0.997 0.000600 0.999 0.000866 -#> 8 -0.0733 -0.0730 0.997 0.0007 0.999 0.00101 -#> 9 -0.0733 -0.0730 0.996 0.0008 0.999 0.00115 -#> 10 -0.0733 -0.0730 0.996 0.0009 0.999 0.00130 +#> bob$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.0732 -0.0732 0 0 1 0 +#> 2 -0.0732 -0.0731 0.0000334 0.0001 1.000 0.000144 +#> 3 -0.0732 -0.0731 0.0000668 0.0002 1.000 0.000289 +#> 4 -0.0732 -0.0731 0.000100 0.000300 1.000 0.000433 +#> 5 -0.0732 -0.0731 0.000134 0.0004 1.000 0.000577 +#> 6 -0.0732 -0.0731 0.000167 0.0005 1.000 0.000722 +#> 7 -0.0733 -0.0731 0.000200 0.000600 0.999 0.000866 +#> 8 -0.0733 -0.0730 0.000234 0.0007 0.999 0.00101 +#> 9 -0.0733 -0.0730 0.000267 0.0008 0.999 0.00115 +#> 10 -0.0733 -0.0730 0.000301 0.0009 0.999 0.00130 #> # … with 9,990 more rows
    diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html index 496a1a5..06dfe8c 100644 --- a/docs/reference/curve_lik.html +++ b/docs/reference/curve_lik.html @@ -239,9 +239,9 @@

    Examp #> 10 0.714 -3.34 0.00381 11.1 #> # … with 343 more rows
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
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    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"): Error: 'xaxis' must be a string.
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
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    +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"): Error: 'xaxis' must be a string.
    #> # A tibble: 10,000 x 1 -#> bob$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.919 -0.919 1 0 1 0 -#> 2 -0.919 -0.918 0.999 0.0001 1.000 0.000144 -#> 3 -0.920 -0.917 0.998 0.0002 1.000 0.000289 -#> 4 -0.920 -0.917 0.997 0.000300 1.000 0.000433 -#> 5 -0.921 -0.916 0.995 0.0004 1.000 0.000577 -#> 6 -0.921 -0.916 0.994 0.0005 1.000 0.000722 -#> 7 -0.922 -0.915 0.993 0.000600 0.999 0.000866 -#> 8 -0.922 -0.915 0.992 0.0007 0.999 0.00101 -#> 9 -0.923 -0.914 0.991 0.0008 0.999 0.00115 -#> 10 -0.923 -0.914 0.990 0.0009 0.999 0.00130 +#> bob$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.919 -0.919 0 0 1 0 +#> 2 -0.919 -0.918 0.00104 0.0001 1.000 0.000144 +#> 3 -0.920 -0.917 0.00208 0.0002 1.000 0.000289 +#> 4 -0.920 -0.917 0.00313 0.000300 1.000 0.000433 +#> 5 -0.921 -0.916 0.00417 0.0004 1.000 0.000577 +#> 6 -0.921 -0.916 0.00521 0.0005 1.000 0.000722 +#> 7 -0.922 -0.915 0.00625 0.000600 0.999 0.000866 +#> 8 -0.922 -0.915 0.00729 0.0007 0.999 0.00101 +#> 9 -0.923 -0.914 0.00834 0.0008 0.999 0.00115 +#> 10 -0.923 -0.914 0.00938 0.0009 0.999 0.00130 #> # … with 9,990 more rows
    diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html index 687e346..25cf577 100644 --- a/docs/reference/curve_meta.html +++ b/docs/reference/curve_meta.html @@ -242,18 +242,18 @@

    Examp metaf <- curve_meta(res) tibble::tibble(metaf)
    #> # A tibble: 10,000 x 1 -#> metaf$lower.limit $upper.limit $intrvl.level $pvalue $svalue $limit.ratio -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.120 0.120 0 1 0 1 -#> 2 0.120 0.121 0.0001 1.000 0.000144 1.00 -#> 3 0.120 0.121 0.0002 1.000 0.000289 1.00 -#> 4 0.120 0.121 0.000300 1.000 0.000433 1.00 -#> 5 0.120 0.121 0.0004 1.000 0.000577 1.00 -#> 6 0.120 0.121 0.0005 1.000 0.000722 1.00 -#> 7 0.120 0.121 0.000600 0.999 0.000866 1.00 -#> 8 0.120 0.121 0.0007 0.999 0.00101 1.00 -#> 9 0.120 0.121 0.0008 0.999 0.00115 1.00 -#> 10 0.120 0.121 0.0009 0.999 0.00130 1.00 +#> metaf$lower.limit $upper.limit $intrvl.level $pvalue $svalue $intrvl.width +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.120 0.120 0 1 0 0 +#> 2 0.120 0.121 0.0001 1.000 0.000144 0.0000564 +#> 3 0.120 0.121 0.0002 1.000 0.000289 0.000113 +#> 4 0.120 0.121 0.000300 1.000 0.000433 0.000169 +#> 5 0.120 0.121 0.0004 1.000 0.000577 0.000226 +#> 6 0.120 0.121 0.0005 1.000 0.000722 0.000282 +#> 7 0.120 0.121 0.000600 0.999 0.000866 0.000339 +#> 8 0.120 0.121 0.0007 0.999 0.00101 0.000395 +#> 9 0.120 0.121 0.0008 0.999 0.00115 0.000451 +#> 10 0.120 0.121 0.0009 0.999 0.00130 0.000508 #> # … with 9,990 more rows
    #> # A tibble: 9,999 x 1 -#> df$lower.limit $upper.limit $limit.ratio $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.624 4.15 6.65 1.000 0.0001000 13.3 -#> 2 0.651 3.98 6.12 1.000 0.000200 12.3 -#> 3 0.667 3.88 5.82 1.000 0.000300 11.7 -#> 4 0.680 3.81 5.61 1.000 0.000400 11.3 -#> 5 0.690 3.76 5.45 1.000 0.000500 11.0 -#> 6 0.698 3.71 5.32 0.999 0.0006 10.7 -#> 7 0.705 3.68 5.21 0.999 0.0007 10.5 -#> 8 0.712 3.64 5.12 0.999 0.0008 10.3 -#> 9 0.717 3.61 5.04 0.999 0.0009 10.1 -#> 10 0.722 3.59 4.97 0.999 0.001 9.97 +#> df$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.624 4.15 3.53 1.000 0.0001000 13.3 +#> 2 0.651 3.98 3.33 1.000 0.000200 12.3 +#> 3 0.667 3.88 3.22 1.000 0.000300 11.7 +#> 4 0.680 3.81 3.13 1.000 0.000400 11.3 +#> 5 0.690 3.76 3.07 1.000 0.000500 11.0 +#> 6 0.698 3.71 3.02 0.999 0.0006 10.7 +#> 7 0.705 3.68 2.97 0.999 0.0007 10.5 +#> 8 0.712 3.64 2.93 0.999 0.0008 10.3 +#> 9 0.717 3.61 2.90 0.999 0.0009 10.1 +#> 10 0.722 3.59 2.87 0.999 0.001 9.97 #> # … with 9,989 more rows
    diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html index b747ce2..f7dea2f 100644 --- a/docs/reference/curve_table.html +++ b/docs/reference/curve_table.html @@ -210,16 +210,16 @@

    Examp data = RandomData, method = "default" ) -(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Lower Limit Upper Limit Limit Ratio Interval Level (%) P-value -#> 2501 0.043 0.083 1.911 25.0 0.750 -#> 5001 0.021 0.105 4.930 50.0 0.500 -#> 7501 -0.008 0.135 -16.319 75.0 0.250 -#> 8001 -0.016 0.143 -8.704 80.0 0.200 -#> 8501 -0.026 0.153 -5.819 85.0 0.150 -#> 9001 -0.039 0.166 -4.241 90.0 0.100 -#> 9501 -0.059 0.185 -3.156 95.0 0.050 -#> 9751 -0.076 0.203 -2.660 97.5 0.025 -#> 9901 -0.097 0.224 -2.303 99.0 0.010 +(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Lower Limit Upper Limit Interval Width Interval Level (%) P-value +#> 2501 0.043 0.083 0.040 25.0 0.750 +#> 5001 0.021 0.105 0.084 50.0 0.500 +#> 7501 -0.008 0.135 0.143 75.0 0.250 +#> 8001 -0.016 0.143 0.159 80.0 0.200 +#> 8501 -0.026 0.153 0.179 85.0 0.150 +#> 9001 -0.039 0.166 0.205 90.0 0.100 +#> 9501 -0.059 0.185 0.244 95.0 0.050 +#> 9751 -0.076 0.203 0.279 97.5 0.025 +#> 9901 -0.097 0.224 0.321 99.0 0.010 #> S-value (bits) #> 2501 0.415 #> 5001 1.000 @@ -230,40 +230,40 @@

    Examp #> 9501 4.322 #> 9751 5.322 #> 9901 6.644

    (z <- curve_table(intervalsdf, type = "tibble"))
    #> # A tibble: 9 x 1 -#> subdf$`Lower Li… $`Upper Limit` $`Limit Ratio` $`Interval Leve… $`P-value` -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.043 0.083 1.91 25 0.75 -#> 2 0.021 0.105 4.93 50 0.5 -#> 3 -0.008 0.135 -16.3 75 0.25 -#> 4 -0.016 0.143 -8.70 80 0.2 -#> 5 -0.026 0.153 -5.82 85 0.15 -#> 6 -0.039 0.166 -4.24 90 0.1 -#> 7 -0.059 0.185 -3.16 95 0.05 -#> 8 -0.076 0.203 -2.66 97.5 0.025 -#> 9 -0.097 0.224 -2.30 99 0.01 +#> subdf$`Lower Li… $`Upper Limit` $`Interval Widt… $`Interval Leve… $`P-value` +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.043 0.083 0.04 25 0.75 +#> 2 0.021 0.105 0.084 50 0.5 +#> 3 -0.008 0.135 0.143 75 0.25 +#> 4 -0.016 0.143 0.159 80 0.2 +#> 5 -0.026 0.153 0.179 85 0.15 +#> 6 -0.039 0.166 0.205 90 0.1 +#> 7 -0.059 0.185 0.244 95 0.05 +#> 8 -0.076 0.203 0.279 97.5 0.025 +#> 9 -0.097 0.224 0.321 99 0.01 #> # … with 1 more variable: $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf, type = "latex"))
    #> #> -#> | | Lower Limit| Upper Limit| Limit Ratio| Interval Level (%)| P-value| S-value (bits)| -#> |:----|-----------:|-----------:|-----------:|------------------:|-------:|--------------:| -#> |2501 | 0.043| 0.083| 1.911| 25.0| 0.750| 0.415| -#> |5001 | 0.021| 0.105| 4.930| 50.0| 0.500| 1.000| -#> |7501 | -0.008| 0.135| -16.319| 75.0| 0.250| 2.000| -#> |8001 | -0.016| 0.143| -8.704| 80.0| 0.200| 2.322| -#> |8501 | -0.026| 0.153| -5.819| 85.0| 0.150| 2.737| -#> |9001 | -0.039| 0.166| -4.241| 90.0| 0.100| 3.322| -#> |9501 | -0.059| 0.185| -3.156| 95.0| 0.050| 4.322| -#> |9751 | -0.076| 0.203| -2.660| 97.5| 0.025| 5.322| -#> |9901 | -0.097| 0.224| -2.303| 99.0| 0.010| 6.644|
    (z <- curve_table(intervalsdf, type = "image"))
    #> a flextable object. -#> col_keys: `Lower Limit`, `Upper Limit`, `Limit Ratio`, `Interval Level (%)`, `P-value`, `S-value (bits)` +#> | | Lower Limit| Upper Limit| Interval Width| Interval Level (%)| P-value| S-value (bits)| +#> |:----|-----------:|-----------:|--------------:|------------------:|-------:|--------------:| +#> |2501 | 0.043| 0.083| 0.040| 25.0| 0.750| 0.415| +#> |5001 | 0.021| 0.105| 0.084| 50.0| 0.500| 1.000| +#> |7501 | -0.008| 0.135| 0.143| 75.0| 0.250| 2.000| +#> |8001 | -0.016| 0.143| 0.159| 80.0| 0.200| 2.322| +#> |8501 | -0.026| 0.153| 0.179| 85.0| 0.150| 2.737| +#> |9001 | -0.039| 0.166| 0.205| 90.0| 0.100| 3.322| +#> |9501 | -0.059| 0.185| 0.244| 95.0| 0.050| 4.322| +#> |9751 | -0.076| 0.203| 0.279| 97.5| 0.025| 5.322| +#> |9901 | -0.097| 0.224| 0.321| 99.0| 0.010| 6.644|
    (z <- curve_table(intervalsdf, type = "image"))
    #> a flextable object. +#> col_keys: `Lower Limit`, `Upper Limit`, `Interval Width`, `Interval Level (%)`, `P-value`, `S-value (bits)` #> header has 1 row(s) #> body has 9 row(s) #> original dataset sample: -#> Lower Limit Upper Limit Limit Ratio Interval Level (%) P-value -#> 2501 0.043 0.083 1.911 25 0.75 -#> 5001 0.021 0.105 4.930 50 0.50 -#> 7501 -0.008 0.135 -16.319 75 0.25 -#> 8001 -0.016 0.143 -8.704 80 0.20 -#> 8501 -0.026 0.153 -5.819 85 0.15 +#> Lower Limit Upper Limit Interval Width Interval Level (%) P-value +#> 2501 0.043 0.083 0.040 25 0.75 +#> 5001 0.021 0.105 0.084 50 0.50 +#> 7501 -0.008 0.135 0.143 75 0.25 +#> 8001 -0.016 0.143 0.159 80 0.20 +#> 8501 -0.026 0.153 0.179 85 0.15 #> S-value (bits) #> 2501 0.415 #> 5001 1.000 diff --git a/docs/reference/ggconcurve-1.png b/docs/reference/ggconcurve-1.png index 2d99a38241adc67e19d5057f0a371fb77052b5fc..f5c65fec9473a3be0079325a574636fa24492ab1 100644 GIT binary patch literal 153332 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mvL1&QET!oFk3?)S{vJd4%8#{{>H*dx@TDNDER!v5;PpQ#njeP% diff --git a/docs/reference/ggconcurve.html b/docs/reference/ggconcurve.html index d79e633..0e0f50f 100644 --- a/docs/reference/ggconcurve.html +++ b/docs/reference/ggconcurve.html @@ -233,7 +233,7 @@

    Examp ) -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values")))

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    +(function1 <- ggconcurve(type = "consonance", intervalsdf))
    GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) @@ -243,15 +243,15 @@

    Examp randomframe <- curve_gen(model, "GroupB2") -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))

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    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
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    +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> Error in ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.5, 0.75, 0.85, 0.9, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
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    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."): Error: 'xaxis' must be a string.
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
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    +subtitle = "The function displays deviance intervals at every level."))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "The function displays deviance intervals at every level."): Error: 'xaxis' must be a string.
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    +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values")))
    GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) @@ -260,18 +260,18 @@

    Examp randomframe <- curve_gen(model, "GroupB2") -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))

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    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
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    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    #> Warning: conversion failure on 'Θ Range of Values' in 'mbcsToSbcs': dot substituted for <ce>
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    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
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    +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> Error in ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.5, 0.75, 0.85, 0.9, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    #> Error in plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."): Error: 'xaxis' must be a string.
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    #> Error in plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
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    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."): Error: 'xaxis' must be a string.
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
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    +subtitle = "The function displays deviance intervals at every level."))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "The function displays deviance intervals at every level."): Error: 'xaxis' must be a string.
    -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio"))
    #> Warning: conversion failure on 'Θ = Hazard Ratio / Odds Ratio' in 'mbcsToSbcs': dot substituted for <ce>
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    -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio"))
    #> Warning: conversion failure on 'Θ = Hazard Ratio / Odds Ratio' in 'mbcsToSbcs': dot substituted for <ce>
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    +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> Error in plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")): Error: 'xaxis' must be a string.
    +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> Error in plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")): Error: 'xaxis' must be a string.
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https://data.lesslikely.com/concurve//index.html + + https://data.lesslikely.com/concurve//reference/curve_compare.html + https://data.lesslikely.com/concurve//reference/curve_corr.html diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd new file mode 100644 index 0000000..ab2de0b --- /dev/null +++ b/man/curve_compare.Rd @@ -0,0 +1,61 @@ +\name{curve_compare} +\alias{curve_compare} +%- Also NEED an '\alias' for EACH other topic documented here. +\title{ +Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets +} +\description{ +%% ~~ A concise (1-5 lines) description of what the function does. ~~ +} +\usage{ +curve_compare(data1, data2, type = "consonance", plot = TRUE, ...) +} +%- maybe also 'usage' for other objects documented here. +\arguments{ + \item{data1}{ +%% ~~Describe \code{data1} here~~ +} + \item{data2}{ +%% ~~Describe \code{data2} here~~ +} + \item{type}{ +%% ~~Describe \code{type} here~~ +} + \item{plot}{ +%% ~~Describe \code{plot} here~~ +} + \item{\dots}{ +%% ~~Describe \code{\dots} here~~ +} +} +\details{ +%% ~~ If necessary, more details than the description above ~~ +} +\value{ +%% ~Describe the value returned +%% If it is a LIST, use +%% \item{comp1 }{Description of 'comp1'} +%% \item{comp2 }{Description of 'comp2'} +%% ... +} +\references{ +%% ~put references to the literature/web site here ~ +} +\author{ +%% ~~who you are~~ +} +\note{ +%% ~~further notes~~ +} + +%% ~Make other sections like Warning with \section{Warning }{....} ~ + +\seealso{ +%% ~~objects to See Also as \code{\link{help}}, ~~~ +} +\examples{ +} +% Add one or more standard keywords, see file 'KEYWORDS' in the +% R documentation directory. +\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") +\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index 9b0717a..73f3f1c 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -79,7 +79,7 @@ intervalsdf <- curve_mean(GroupA, GroupB, ) -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values"))) +(function1 <- ggconcurve(type = "consonance", intervalsdf)) GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd index b33117f..0be28b9 100644 --- a/man/plot_compare.Rd +++ b/man/plot_compare.Rd @@ -91,7 +91,7 @@ intervalsdf <- curve_mean(GroupA, GroupB, ) -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values"))) +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values"))) GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) @@ -118,9 +118,9 @@ lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps subtitle = "The function displays deviance intervals at every level.")) -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio")) +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio"))) -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio")) +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio"))) } From e5bafb445f8a1ab8b24696c505856acdfbcff745 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Mon, 25 Nov 2019 04:31:18 -0500 Subject: [PATCH 05/21] curve_compare and updated website --- R/ggconcurve.R | 9 --------- R/plot_compare.R | 12 ------------ docs/reference/curve_lik-1.png | Bin 119902 -> 119953 bytes docs/reference/curve_lik-2.png | Bin 112610 -> 112678 bytes docs/reference/curve_lik.html | 4 ++-- docs/reference/ggconcurve-3.png | Bin 148986 -> 147813 bytes docs/reference/ggconcurve-4.png | Bin 120447 -> 120488 bytes docs/reference/ggconcurve-5.png | Bin 113754 -> 113842 bytes docs/reference/ggconcurve.html | 6 +++--- docs/reference/plot_compare-3.png | Bin 147467 -> 146539 bytes docs/reference/plot_compare-4.png | Bin 183267 -> 182635 bytes docs/reference/plot_compare-5.png | Bin 191728 -> 190828 bytes docs/reference/plot_compare-6.png | Bin 120447 -> 120488 bytes docs/reference/plot_compare-7.png | Bin 113754 -> 113842 bytes docs/reference/plot_compare-8.png | Bin 145043 -> 145651 bytes docs/reference/plot_compare-9.png | Bin 140867 -> 140638 bytes docs/reference/plot_compare.html | 14 +++++++------- 17 files changed, 12 insertions(+), 33 deletions(-) diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 1a0b20c..e7206d5 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -133,9 +133,6 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } @@ -211,9 +208,6 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } @@ -277,9 +271,6 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } diff --git a/R/plot_compare.R b/R/plot_compare.R index 5d8a75e..472464b 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -38,9 +38,6 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } @@ -175,9 +172,6 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } @@ -279,9 +273,6 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } @@ -369,9 +360,6 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (is.character(subtitle) != TRUE) { stop("Error: 'subtitle' must be a string.") } - if (is.character(xaxis) != TRUE) { - stop("Error: 'xaxis' must be a string.") - } if (is.character(yaxis) != TRUE) { stop("Error: 'yaxis' must be a string.") } diff --git a/docs/reference/curve_lik-1.png b/docs/reference/curve_lik-1.png index e199e52e3a4771112a606aaaf8b3d993366d4de3..b9050d826ba2dd5dfd7fd62967c6de94f2b672af 100644 GIT binary patch literal 119953 zcmeFZXH-+!8#f9lpdg~s6r?yx7f?DV1Vur5FH)5vHS`*~fFOvdGy&3YMnFJ7A@}5w zIspN(2LZtulXE2C6Lf6aIsw7iYgW?Isy5QH()PCYP8tr+P0eM^?aZC5Ox0x{5)g<+ zL}(dWUe}_QtZ}xud8=E)RxsEl7ec{G5v47dR)+8!9~ZWZl@lKQW6^A9wfp0bZc%}n zqcoMrs@``oiPS9(mHsRoI7+l+8D9(;>nM_iL$YiyP<1gp8ykO5Al?d5GGV^zy_K|< 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zumvwIYal$0k~|&3(p~acq9~x#;unAjS>1%(n*s*L{rs>>-FhZ|@T*bfB3GfQWCw~$ zJ{?LpntXgnpp<_l2e-2EPF#TN#g$uKx-^yesQq-DjpgQk1sPx*A-fI z)B^X$C8^q7^V46<-Q{+7w9e->WS@5)58O?c|8b~)Bo|DBSjoN6 zf1XXxT^d1u)zP6s;K=Uh(*Hw$%z&}NSX#1^x&Lv2-E*8FD~M+oy+)6;Uq9Qw4iaF4 zLWb7`HTf0X^|z6Olg+u$3pjA*H7fX f?&<$*T6Vs(Vnso5;zbWP@nB4mp=NYiG diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html index 06dfe8c..ae36f7b 100644 --- a/docs/reference/curve_lik.html +++ b/docs/reference/curve_lik.html @@ -239,9 +239,9 @@

    Examp #> 10 0.714 -3.34 0.00381 11.1 #> # … with 343 more rows
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"): Error: 'xaxis' must be a string.
    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"): Error: 'xaxis' must be a string.
    +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> Error in ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.5, 0.75, 0.85, 0.9, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."): Error: 'xaxis' must be a string.
    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "The function displays deviance intervals at every level."): Error: 'xaxis' must be a string.
    +subtitle = "The function displays deviance intervals at every level."))
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> Error in ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.5, 0.75, 0.85, 0.9, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    #> Error in plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."): Error: 'xaxis' must be a string.
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    #> Error in plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."): Error: 'xaxis' must be a string.
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> Error in ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."): Error: 'xaxis' must be a string.
    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
    #> Error in ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "The function displays deviance intervals at every level."): Error: 'xaxis' must be a string.
    +subtitle = "The function displays deviance intervals at every level."))
    -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> Error in plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")): Error: 'xaxis' must be a string.
    -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> Error in plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio")): Error: 'xaxis' must be a string.
    +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    From 26ece4151a693a26f092cb9c8f2aa8980d8ba74e Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 26 Nov 2019 04:56:21 -0500 Subject: [PATCH 06/21] Now plots likelihoods, relative likelihoods, and log-likelihoods --- DESCRIPTION | 4 +- R/curve_compare.R | 10 +- R/curve_lik.R | 7 +- R/defunct.R | 6 +- R/ggconcurve.R | 192 +++++++++++++++++++++++++++------ R/plot_compare.R | 262 ++++++++++++++++++++++++++++++++++++++-------- R/plot_concurve.R | 4 +- codemeta.json | 7 +- vitd.R | 55 ++++++++++ 9 files changed, 457 insertions(+), 90 deletions(-) create mode 100644 vitd.R diff --git a/DESCRIPTION b/DESCRIPTION index f2d845a..4988a0c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,6 +14,7 @@ Authors@R: c( Maintainer: Zad R. Chow Description: Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) , Schweder T, Hjort NL. (2002) , Singh K, Xie M, Strawderman WE. (2007) , Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) , Greenland S. (2019) , Chow ZR, Greenland S. (2019) , and Greenland S, Chow ZR. (2019) . Imports: parallel, + compiler, ggplot2, metafor, dplyr, @@ -33,6 +34,7 @@ URL: https://data.lesslikely.com/concurve/, https://github.com/Zadchow/concurve, BugReports: https://github.com/Zadchow/concurve/issues VignetteBuilder: knitr Encoding: UTF-8 +ByteCompile: true LazyData: true -RoxygenNote: 6.1.1 +RoxygenNote: 7.0.1 X-schema.org-keywords: confidence, compatibility, consonance, surprisal, interval, function, curve diff --git a/R/curve_compare.R b/R/curve_compare.R index 2012925..56eaea1 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -1,8 +1,8 @@ -curve_compare <- function(data1, data2, type = "consonance", plot = TRUE, ...) { +curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { # Consonance Function ----------------------------------------------------- - if (type == "consonance") { + if (type == "c") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } @@ -16,7 +16,7 @@ curve_compare <- function(data1, data2, type = "consonance", plot = TRUE, ...) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (plot == TRUE) { - plot_comparison <- (plot_compare(data1, data2, type = "consonance", ...)) + plot_comparison <- (plot_compare(data1, data2, type = "c", ...)) } else if (plot == FALSE) { } @@ -67,7 +67,7 @@ curve_compare <- function(data1, data2, type = "consonance", plot = TRUE, ...) { # Surprisal Function ------------------------------------------------------ - } else if (type == "surprisal") { + } else if (type == "s") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } @@ -82,7 +82,7 @@ curve_compare <- function(data1, data2, type = "consonance", plot = TRUE, ...) { } if (plot == TRUE) { - plot_comparison <- (plot_compare(data1, data2, type = "surprisal", ...)) + plot_comparison <- (plot_compare(data1, data2, type = "s", ...)) } else if (plot == FALSE) { } diff --git a/R/curve_lik.R b/R/curve_lik.R index 3391fb6..a79f2d9 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -51,9 +51,10 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { support <- exp((-zscore^2) / 2) deviancestat <- (zscore^2) - - likfunction <- data.frame(values, zscore, support, deviancestat) + likelihood <- support * (log(point)) + loglikelihood <- log(likelihood) + likfunction <- data.frame(values, zscore, likelihood, loglikelihood, support, deviancestat) return(likfunction) } -utils::globalVariables(c("likfunction", "values", "zscore", "support", "deviancestat")) +utils::globalVariables(c("likfunction", "values", "zscore", "likelihood", "loglikelihood", "support", "deviancestat")) diff --git a/R/defunct.R b/R/defunct.R index 2d15220..24cc3bb 100644 --- a/R/defunct.R +++ b/R/defunct.R @@ -1,5 +1,7 @@ -defunct <- function(msg = "This function is deprecated") function(...) { - return(stop(msg)) +defunct <- function(msg = "This function is deprecated") { + function(...) { + return(stop(msg)) + } } # Graphical functions diff --git a/R/ggconcurve.R b/R/ggconcurve.R index e7206d5..7108133 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -1,14 +1,14 @@ -ggconcurve <- function(data, type = "consonance", measure = "default", levels = 0.95, nullvalue = "absent", position = "pyramid", +ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nullvalue = FALSE, position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", - xaxis = expression(Theta~"Range of Values"), + xaxis = expression(Theta ~ "Range of Values"), yaxis = "P-value", - color = "#555555", + color = "#000000", fill = "#239a98") { # Consonance Function ----------------------------------------------------- - if (type == "consonance") { + if (type == "c") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } @@ -18,8 +18,8 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(position) != TRUE) { stop("Error: 'position' must be a string such as 'pyramid' or 'inverted'.") @@ -50,11 +50,11 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = ggplot(data = data) + - geom_point(aes(x = lower.limit, y = pvalue), - color = color, fill = fill, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = lower.limit, y = pvalue), + color = color ) + - geom_point(aes(x = upper.limit, y = pvalue), - color = color, fill = fill, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = upper.limit, y = pvalue), + color = color ) + geom_point(data = interval, mapping = aes(x = limits, y = 1 - levels), size = .95) + geom_line(data = interval, mapping = aes(x = limits, y = 1 - levels, group = levels), size = .40) + @@ -100,24 +100,24 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = ) } } + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } - else if (nullvalue == "absent") { + else if (nullvalue == FALSE) { } # Surprisal Function ------------------------------------------------------ - } else if (type == "surprisal") { + } else if (type == "s") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } @@ -151,11 +151,11 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = colnames(interval) <- colum_names ggplot(data = data) + - geom_point(aes(x = lower.limit, y = svalue), - color = color, fill = fill, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = lower.limit, y = svalue), + color = color ) + - geom_point(aes(x = upper.limit, y = svalue), - color = color, fill = fill, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = upper.limit, y = svalue), + color = color ) + geom_point(data = interval, mapping = aes(x = limits, y = (-log2(1 - levels))), size = .95) + geom_line(data = interval, mapping = aes(x = limits, y = (-log2(1 - levels)), group = levels), size = .40) + @@ -188,19 +188,19 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) - # Likelihood Function ----------------------------------------------------- - } else if (type == "likelihood") { + # Relative Likelihood Function ----------------------------------------------------- + } else if (type == "l1") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } - if (ncol(data) != 4) { + if (ncol(data) != 6) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(title) != TRUE) { stop("Error: 'title' must be a string.") @@ -219,7 +219,7 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = geom_line() + geom_ribbon(aes(x = values, ymin = min(support), ymax = support), fill = "#239a98", alpha = 0.30) + labs( - title = "Likelihood Function", + title = "Relative Likelihood Function", subtitle = subtitle, x = xaxis, y = "Relative Likelihood \n(1/MLR)" @@ -237,33 +237,159 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } + } + + # Log-Likelihood Function ----------------------------------------------------- + } else if (type == "l2") { + if (is.data.frame(data) != TRUE) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (ncol(data) != 6) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill) != TRUE) { + stop("Error: 'fill' must be a string for the color.") + } + + ggplot(data = data, mapping = aes(x = values, y = loglikelihood)) + + geom_line() + + geom_ribbon(aes(x = values, ymin = min(loglikelihood), ymax = loglikelihood), fill = "#239a98", alpha = 0.30) + + labs( + title = "Log-Likelihood Function", + subtitle = subtitle, + x = xaxis, + y = "Log Likelihood" + ) + + theme_bw() + + theme( + plot.title = element_text(size = 16), + plot.subtitle = element_text(size = 12), + plot.caption = element_text(size = 8), + axis.title.x = element_text(size = 13), + axis.title.y = element_text(size = 13), + text = element_text(size = 15) + ) + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + + if (nullvalue == TRUE) { + if (measure == "default") { + annotate("segment", + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } else if (measure == "ratio") { + annotate("segment", + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } + } + + # Likelihood Function ----------------------------------------------------- + } else if (type == "l3") { + if (is.data.frame(data) != TRUE) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (ncol(data) != 6) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill) != TRUE) { + stop("Error: 'fill' must be a string for the color.") + } + + ggplot(data = data, mapping = aes(x = values, y = likelihood)) + + geom_line() + + geom_ribbon(aes(x = values, ymin = min(likelihood), ymax = likelihood), fill = "#239a98", alpha = 0.30) + + labs( + title = "Likelihood Function", + subtitle = subtitle, + x = xaxis, + y = "Likelihood" + ) + + theme_bw() + + theme( + plot.title = element_text(size = 16), + plot.subtitle = element_text(size = 12), + plot.caption = element_text(size = 8), + axis.title.x = element_text(size = 13), + axis.title.y = element_text(size = 13), + text = element_text(size = 15) + ) + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + + if (nullvalue == TRUE) { + if (measure == "default") { + annotate("segment", + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } else if (measure == "ratio") { + annotate("segment", + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } # Deviance Function ----------------------------------------------------- - } else if (type == "deviance") { + } else if (type == "d") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } - if (ncol(data) != 4) { + if (ncol(data) != 6) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(title) != TRUE) { stop("Error: 'title' must be a string.") @@ -300,7 +426,7 @@ ggconcurve <- function(data, type = "consonance", measure = "default", levels = if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, diff --git a/R/plot_compare.R b/R/plot_compare.R index 472464b..2e3ca77 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -1,16 +1,16 @@ -plot_compare <- function(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", +plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalue = FALSE, position = "pyramid", title = "Interval Functions", subtitle = "The function displays intervals at every level.", - xaxis = expression(Theta~"Range of Values"), + xaxis = expression(Theta ~ "Range of Values"), yaxis = "P-value", - color = "#555555", + color = "#000000", fill1 = "#239a98", fill2 = "#d46c5b") { cols <- c(fill1, fill2) # Consonance Function ----------------------------------------------------- - if (type == "consonance") { + if (type == "c") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } @@ -26,8 +26,8 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(position) != TRUE) { stop("Error: 'position' must be a string such as 'pyramid' or 'inverted'.") @@ -48,11 +48,11 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", stop("Error: 'fill2' must be a string for the color.") } ggplot(data = data1) + - geom_point(aes(x = lower.limit, y = pvalue), - color = color, fill = fill1, alpha = 0.5, size = 0.1 + geom_line(aes(x = lower.limit, y = pvalue), + color = color ) + - geom_point(aes(x = upper.limit, y = pvalue), - color = color, fill = fill1, alpha = 0.5, size = 0.1 + geom_line(aes(x = upper.limit, y = pvalue), + color = color ) + geom_ribbon(aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue, fill = fill1), alpha = 0.30 @@ -60,13 +60,13 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", geom_ribbon(aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue, fill = fill1), alpha = 0.30 ) + - geom_point( + geom_line( data = data2, aes(x = lower.limit, y = pvalue), - color = color, fill = fill2, alpha = 0.5, size = 0.1 + color = color ) + - geom_point( + geom_line( data = data2, aes(x = upper.limit, y = pvalue), - color = color, fill = fill2, alpha = 0.5, size = 0.1 + color = color ) + geom_ribbon( data = data2, aes(x = lower.limit, ymin = min(pvalue), ymax = pvalue, fill = fill2), @@ -132,25 +132,25 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", ) } } + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } - else if (nullvalue == "absent") { + else if (nullvalue == FALSE) { } # Surprisal Function ------------------------------------------------------ - } else if (type == "surprisal") { + } else if (type == "s") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } @@ -182,11 +182,11 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", stop("Error: 'fill2' must be a string for the color.") } ggplot(data = data1) + - geom_point(aes(x = lower.limit, y = svalue), - color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = lower.limit, y = svalue), + color = color ) + - geom_point(aes(x = upper.limit, y = svalue), - color = color, fill = fill1, alpha = 0.5, shape = 20, size = 0.1 + geom_line(aes(x = upper.limit, y = svalue), + color = color ) + geom_ribbon(aes(x = lower.limit, ymin = max(svalue), ymax = svalue, fill = fill1), alpha = 0.30 @@ -194,13 +194,13 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", geom_ribbon(aes(x = upper.limit, ymin = max(svalue), ymax = svalue, fill = fill1), alpha = 0.30 ) + - geom_point( + geom_line( data = data2, aes(x = lower.limit, y = svalue), - color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + color = color ) + - geom_point( + geom_line( data = data2, aes(x = upper.limit, y = svalue), - color = color, fill = fill2, alpha = 0.5, shape = 20, size = 0.1 + color = color ) + geom_ribbon( data = data2, aes(x = lower.limit, ymin = max(svalue), ymax = svalue, fill = fill2), @@ -253,19 +253,19 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) - # Likelihood Function ----------------------------------------------------- - } else if (type == "likelihood") { - if (ncol(data1) != 4) { + # Relative Likelihood Function ----------------------------------------------------- + } else if (type == "l1") { + if (ncol(data1) != 6) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 4) { + if (ncol(data2) != 6) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(title) != TRUE) { stop("Error: 'title' must be a string.") @@ -289,7 +289,7 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", geom_line(data = data2) + geom_ribbon(data = data2, aes(x = values, ymin = min(support), ymax = support, fill = fill2), alpha = 0.30) + labs( - title = "Likelihood Functions", + title = "Relative Likelihood Functions", subtitle = subtitle, x = xaxis, y = "Relative Likelihood \n(1/MLR)" @@ -326,33 +326,209 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .99, linetype = 1 + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } + } + + + # Log-Likelihood Function ----------------------------------------------------- + } else if (type == "l2") { + if (ncol(data1) != 6) { + stop("Error: 'data1' must be a data frame from 'concurve'.") + } + if (ncol(data2) != 6) { + stop("Error: 'data2' must be a data frame from 'concurve'.") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill1) != TRUE) { + stop("Error: 'fill1' must be a string for the color.") + } + if (is.character(fill2) != TRUE) { + stop("Error: 'fill2' must be a string for the color.") + } + + ggplot(data = data1, mapping = aes(x = values, y = loglikelihood)) + + geom_line() + + geom_ribbon(aes(x = values, ymin = min(loglikelihood), ymax = loglikelihood, fill = fill1), alpha = 0.30) + + geom_line(data = data2) + + geom_ribbon(data = data2, aes(x = values, ymin = min(loglikelihood), ymax = loglikelihood, fill = fill2), alpha = 0.30) + + labs( + title = "Log-Likelihood Functions", + subtitle = subtitle, + x = xaxis, + y = "Log-Likelihood" + ) + + theme_bw() + + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11), + legend.background = element_blank(), + legend.position = c(.998, .998), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") + ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + + if (nullvalue == TRUE) { + if (measure == "default") { + annotate("segment", + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } else if (measure == "ratio") { + annotate("segment", + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } + } + + + # Likelihood Function ----------------------------------------------------- + } else if (type == "l3") { + if (ncol(data1) != 6) { + stop("Error: 'data1' must be a data frame from 'concurve'.") + } + if (ncol(data2) != 6) { + stop("Error: 'data2' must be a data frame from 'concurve'.") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill1) != TRUE) { + stop("Error: 'fill1' must be a string for the color.") + } + if (is.character(fill2) != TRUE) { + stop("Error: 'fill2' must be a string for the color.") + } + + ggplot(data = data1, mapping = aes(x = values, y = likelihood)) + + geom_line() + + geom_ribbon(aes(x = values, ymin = min(likelihood), ymax = likelihood, fill = fill1), alpha = 0.30) + + geom_line(data = data2) + + geom_ribbon(data = data2, aes(x = values, ymin = min(likelihood), ymax = likelihood, fill = fill2), alpha = 0.30) + + labs( + title = "Likelihood Functions", + subtitle = subtitle, + x = xaxis, + y = "Likelihood" + ) + + theme_bw() + + theme( + plot.title = element_text(size = 12), + plot.subtitle = element_text(size = 11), + axis.title.x = element_text(size = 12), + axis.title.y = element_text(size = 12), + text = element_text(size = 11), + legend.background = element_blank(), + legend.position = c(.998, .998), + legend.justification = c("right", "top"), + legend.key = element_rect(linetype = 1), + legend.key.size = unit(0.495, "cm") + ) + + scale_fill_manual( + aesthetics = "fill", + values = cols, + labels = c("Study 1", "Study 2") + ) + + guides(fill = guide_legend( + title = "Identity", + title.theme = element_text( + size = 8 + ), + label.theme = element_text( + size = 8 + ), + label.hjust = 4.5 + )) + + { + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + } + + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + + if (nullvalue == TRUE) { + if (measure == "default") { + annotate("segment", + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 + ) + } else if (measure == "ratio") { + annotate("segment", + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } # Deviance Function ----------------------------------------------------- - } else if (type == "deviance") { - if (ncol(data1) != 4) { + } else if (type == "d") { + if (ncol(data1) != 6) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 4) { + if (ncol(data2) != 6) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") } - if (is.character(nullvalue) != TRUE) { - stop("Error: 'nullvalue' must be a string such as 'absent' or 'present'.") + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") } if (is.character(title) != TRUE) { stop("Error: 'title' must be a string.") @@ -413,7 +589,7 @@ plot_compare <- function(data1, data2, type = "consonance", measure = "default", if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) + - if (nullvalue == "present") { + if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", x = 0, xend = 0, y = 0, yend = 1, diff --git a/R/plot_concurve.R b/R/plot_concurve.R index 4c3b8f2..e083a9c 100644 --- a/R/plot_concurve.R +++ b/R/plot_concurve.R @@ -1,9 +1,9 @@ plot_concurve <- function(data, - type = "consonance", + type = "c", measure = "default", intervals = FALSE, title = "Consonance Function", - xlab = expression(Theta~"Range of Values"), + xlab = expression(Theta ~ "Range of Values"), ylab1 = "P-value", ylab2 = "Confidence Level (%)", fontsize = 12, diff --git a/codemeta.json b/codemeta.json index 35e434c..f81a674 100644 --- a/codemeta.json +++ b/codemeta.json @@ -86,6 +86,11 @@ "identifier": "parallel", "name": "parallel" }, + { + "@type": "SoftwareApplication", + "identifier": "compiler", + "name": "compiler" + }, { "@type": "SoftwareApplication", "identifier": "ggplot2", @@ -220,7 +225,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3840.811KB", + "fileSize": "3841.184KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/vitd.R b/vitd.R new file mode 100644 index 0000000..caedb28 --- /dev/null +++ b/vitd.R @@ -0,0 +1,55 @@ +df1 <- curve_rev(point = 3.00, LL = 0.13, UL = 70.30, measure = "ratio", steps = 10000) +df2 <- curve_rev(point = 1.31, LL = 0.45, UL = 3.80, measure = "ratio", steps = 10000) +df3 <- curve_rev(point = 0.51, LL = 0.25, UL = 1.02, measure = "ratio", steps = 10000) +df4 <- curve_rev(point = 0.89, LL = 0.75, UL = 1.04, measure = "ratio", steps = 10000) +df5 <- curve_rev(point = 1.05, LL = 0.87, UL = 1.26, measure = "ratio", steps = 10000) +df6 <- curve_rev(point = 0.24, LL = 0.03, UL = 2.10, measure = "ratio", steps = 10000) +df7 <- curve_rev(point = 3.70, LL = 1.06, UL = 12.92, measure = "ratio", steps = 10000) +df8 <- curve_rev(point = 0.90, LL = 0.77, UL = 1.07, measure = "ratio", steps = 10000) +df9 <- curve_rev(point = 0.89, LL = 0.68, UL = 1.16, measure = "ratio", steps = 10000) +df10 <- curve_rev(point = 0.50, LL = 0.05, UL = 5.43, measure = "ratio", steps = 10000) +df11 <- curve_rev(point = 1.19, LL = 0.42, UL = 3.33, measure = "ratio", steps = 10000) +df12 <- curve_rev(point = 1.27, LL = 0.64, UL = 2.50, measure = "ratio", steps = 10000) +df13 <- curve_rev(point = 0.33, LL = 0.11, UL = 1.02, measure = "ratio", steps = 10000) +df14 <- curve_rev(point = 0.78, LL = 0.49, UL = 1.26, measure = "ratio", steps = 10000) +df15 <- curve_rev(point = 0.85, LL = 0.56, UL = 1.28, measure = "ratio", steps = 10000) +df16 <- curve_rev(point = 0.32, LL = 0.01, UL = 7.48, measure = "ratio", steps = 10000) +df17 <- curve_rev(point = 2.95, LL = 0.12, UL = 71.60, measure = "ratio", steps = 10000) +df18 <- curve_rev(point = 3.00, LL = 0.13, UL = 70.02, measure = "ratio", steps = 10000) +df19 <- curve_rev(point = 0.35, LL = 0.01, UL = 8.31, measure = "ratio", steps = 10000) +df20 <- curve_rev(point = 0.95, LL = 0.88, UL = 1.02, measure = "ratio", steps = 10000) +df21 <- curve_rev(point = 4.72, LL = 0.23, UL = 97.90, measure = "ratio", steps = 10000) +df22 <- curve_rev(point = 1.50, LL = 0.56, UL = 4.04, measure = "ratio", steps = 10000) +df23 <- curve_rev(point = 0.20, LL = 0.01, UL = 4.21, measure = "ratio", steps = 10000) +df24<- curve_rev(point = 0.33, LL = 0.01, UL = 8.06, measure = "ratio", steps = 10000) +df25 <- curve_rev(point = 0.09, LL = 0.00, UL = 2.08, measure = "ratio", steps = 10000) +df26 <- curve_rev(point = 0.37, LL = 0.02, UL = 8.66, measure = "ratio", steps = 10000) +df27 <- curve_rev(point = 1.25, LL = 0.59, UL = 2.66, measure = "ratio", steps = 10000) +df28 <- curve_rev(point = 2.90, LL = 0.60, UL = 14.09, measure = "ratio", steps = 10000) +df29 <- curve_rev(point = 1.00, LL = 0.14, UL = 7.07, measure = "ratio", steps = 10000) +df30 <- curve_rev(point = 0.48, LL = 0.04, UL = 5.20, measure = "ratio", steps = 10000) +df31 <- curve_rev(point = 2.93, LL = 0.12, UL = 71.47, measure = "ratio", steps = 10000) +df32 <- curve_rev(point = 0.50, LL = 0.05, UL = 5.46, measure = "ratio", steps = 10000) +df33 <- curve_rev(point = 1.00, LL = 0.06, UL = 15.89, measure = "ratio", steps = 10000) +df34 <- curve_rev(point = 1.12, LL = 0.79, UL = 1.58, measure = "ratio", steps = 10000) +df35 <- curve_rev(point = 1.09, LL = 0.73, UL = 1.65, measure = "ratio", steps = 10000) +df36 <- curve_rev(point = 4.91, LL = 0.58, UL = 41.71, measure = "ratio", steps = 10000) +df37 <- curve_rev(point = 0.32, LL = 0.01, UL = 7.79, measure = "ratio", steps = 10000) +df38 <- curve_rev(point = 0.07, LL = 0.00, UL = 1.36, measure = "ratio", steps = 10000) +df39 <- curve_rev(point = 1.01, LL = 0.49, UL = 2.11, measure = "ratio", steps = 10000) +df40 <- curve_rev(point = 0.99, LL = 0.87, UL = 1.11, measure = "ratio", steps = 10000) +df41 <- curve_rev(point = 0.33, LL = 0.01, UL = 8.11, measure = "ratio", steps = 10000) +df42 <- curve_rev(point = 0.95, LL = 0.90, UL = 1.00, measure = "ratio", steps = 10000) +df43 <- curve_rev(point = 1.00, LL = 0.42, UL = 2.41, measure = "ratio", steps = 10000) +df44 <- curve_rev(point = 0.34, LL = 0.01, UL = 8.16, measure = "ratio", steps = 10000) +df45 <- curve_rev(point = 1.57, LL = 0.55, UL = 4.47, measure = "ratio", steps = 10000) +df46 <- curve_rev(point = 1.20, LL = 1.04, UL = 1.37, measure = "ratio", steps = 10000) +df47 <- curve_rev(point = 1.00, LL = 0.87, UL = 1.15, measure = "ratio", steps = 10000) +df48 <- curve_rev(point = 0.99, LL = 0.93, UL = 1.05, measure = "ratio", steps = 10000) +df49 <- curve_rev(point = 0.63, LL = 0.13, UL = 3.07, measure = "ratio", steps = 10000) +df50 <- curve_rev(point = 0.20, LL = 0.01, UL = 4.14, measure = "ratio", steps = 10000) +df51 <- curve_rev(point = 0.33, LL = 0.01, UL = 8.12, measure = "ratio", steps = 10000) +df52 <- curve_rev(point = 1.96, LL = 0.38, UL = 10.26, measure = "ratio", steps = 10000) +df53 <- curve_rev(point = 1.03, LL = 1.09, UL = 70.30, measure = "ratio", steps = 10000) + + From 2a9fac8a10b3b9498694b4c9910c2a423757473b Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Wed, 27 Nov 2019 08:04:32 -0500 Subject: [PATCH 07/21] looking into boot strapping --- .DS_Store | Bin 14340 -> 18436 bytes DESCRIPTION | 2 + NAMESPACE | 2 + R/curve_gen.R | 6 +- R/curve_rev.R | 8 +-- R/curve_sampling.R | 167 +++++++++++++++++++++++++++++++++++++++++++++ R/ggconcurve.R | 29 ++++---- R/plot_compare.R | 41 +++++------ codemeta.json | 26 ++++++- vitd.R | 55 --------------- 10 files changed, 239 insertions(+), 97 deletions(-) create mode 100644 R/curve_sampling.R 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import(ggplot2) +import(ggthemes) +import(Cairo) import(dplyr) import(tidyr) import(flextable) diff --git a/R/curve_gen.R b/R/curve_gen.R index 98c142a..7709094 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -23,11 +23,11 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = } else if (method == "boot") { effect <- coef(model)[[var]] boot_dist <- replicate(replicates, - expr = coef(lm(model$call$formula, - data = model$model[sample(nrow(model$model), replace = T), ] + expr = coef(glm(model$call$formula, + data = model$model[sample(nrow(model$model), family = model$family$family)] ))[[var]] ) - effect - results <- mclapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores(logical = FALSE) - 1) + results <- mclapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores() - 1) } df <- data.frame(do.call(rbind, results)) diff --git a/R/curve_rev.R b/R/curve_rev.R index a997758..18049c5 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -19,8 +19,8 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores(logical = FALSE) - 1) - UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores(logical = FALSE) - 1) + LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores()) + UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores()) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -29,8 +29,8 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores(logical = FALSE) - 1) - logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores(logical = FALSE) - 1) + logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores()) + logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores()) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_sampling.R b/R/curve_sampling.R new file mode 100644 index 0000000..2c8a022 --- /dev/null +++ b/R/curve_sampling.R @@ -0,0 +1,167 @@ +curve_sampling <- function(x, B, func, ..., m = nrow(x), mr = 5, K = 2, J = 10, + alpha, verbose = TRUE) { + call <- match.call() + if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { + stats::runif(1) + } + seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) + if (is.vector(x)) { + x <- as.matrix(x) + } + n <- nrow(x) + nc <- ncol(x) + ttind <- ifelse(length(B) > 1, 1, 0) + if (ttind == 1) { + tt <- B + B <- length(tt) + } + else { + tt <- rep(0, B) + } + t0 <- func(x, ...) + u <- numeric(length = m) + m1 <- sqrt(m * (m - 1)) + if (m == n) { + for (i in seq_len(n)) { + u[i] <- func(x[-i, ], ...) + } + t. <- (mean(u) - u) * (m - 1) + a <- (1 / 6) * sum(t.^3) / (sum(t.^2))^1.5 + sdjack <- sqrt(sum(t.^2)) / m1 + } + if (m < n) { + aa <- ssj <- numeric(mr) + r <- n %% m + seq_len_m <- seq_len(m) + for (k in seq_len(mr)) { + Imat <- sapply(seq_len_m, sample.int, n = n, size = n - + r) + Iout <- setdiff(seq_len(n), Imat) + for (j in seq_len_m) { + Ij <- setdiff(seq_len_m, j) + ij <- c(c(Imat[Ij, ], Iout)) + u[j] <- func(x[ij, ]) + } + t. <- (mean(u) - u) * (m - 1) + aa[k] <- (1 / 6) * sum(t.^3) / (sum(t.^2))^1.5 + ssj[k] <- sqrt(sum(t.^2)) / m1 + } + a <- mean(aa) + sdjack <- mean(ssj) + } + if (ttind == 0) { + tY. <- Y. <- rep(0, n) + if (verbose) { + pb <- utils::txtProgressBar(min = 0, max = B, style = 3) + } + for (j in seq_len(B)) { + ij <- sample(x = n, size = n, replace = TRUE) + Yj <- table(c(ij, 1:n)) - 1 + tt[j] <- func(x[ij, ], ...) + tY. <- tY. + tt[j] * Yj + Y. <- Y. + Yj + if (verbose) { + utils::setTxtProgressBar(pb, j) + } + } + if (verbose) { + close(pb) + } + tt. <- mean(tt) + tY. <- tY. / B + Y. <- Y. / B + s. <- n * (tY. - tt. * Y.) + u. <- 2 * t. - s. + sdu <- sqrt(sum(u.^2)) / n + ustat <- 2 * t0 - tt. + ustats <- c(ustat, sdu) + names(ustats) <- c("ustat", "sdu") + } + B.mean <- c(B, mean(tt)) + alpha <- alpha[alpha < 0.5] + alpha <- c(alpha, 0.5, rev(1 - alpha)) + zalpha <- stats::qnorm(alpha) + nal <- length(alpha) + sdboot0 <- stats::sd(tt) + z00 <- stats::qnorm(sum(tt < t0) / B) + iles <- stats::pnorm(z00 + (z00 + zalpha) / (1 - a * (z00 + + zalpha))) + ooo <- trunc(iles * B) + ooo <- pmin(pmax(ooo, 1), B) + lims0 <- sort(tt)[ooo] + standard <- t0 + sdboot0 * stats::qnorm(alpha) + lims0 <- cbind(lims0, standard) + dimnames(lims0) <- list(alpha, c("bca", "std")) + stats0 <- c(t0, sdboot0, z00, a, sdjack) + names(stats0) <- c("theta", "sdboot", "z0", "a", "sdjack") + vl0 <- list( + lims = lims0, stats = stats0, B.mean = B.mean, + call = call, seed = seed + ) + if (K == 0) { + bcaboot.return(vl0) + } + pct <- rep(0, nal) + for (i in 1:nal) pct[i] <- sum(tt <= lims0[i, 1]) / B + Stand <- vl0$stats[1] + vl0$stats[2] * stats::qnorm(alpha) + Limsd <- matrix(0, length(alpha), K) + Statsd <- matrix(0, 5, K) + for (k in 1:K) { + II <- sample(x = B, size = B) + II <- matrix(II, ncol = J) + lims <- matrix(0, length(alpha), J) + stats <- matrix(0, 5, J) + for (j in 1:J) { + iij <- c(II[, -j]) + ttj <- tt[iij] + Bj <- length(ttj) + sdboot <- stats::sd(ttj) + z0 <- stats::qnorm(sum(ttj < t0) / Bj) + iles <- stats::pnorm(z0 + (z0 + zalpha) / (1 - a * + (z0 + zalpha))) + oo <- trunc(iles * Bj) + oo <- pmin(pmax(oo, 1), Bj) + li <- sort(ttj)[oo] + standard <- t0 + sdboot * stats::qnorm(alpha) + sta <- c(t0, sdboot, z0, a, sdjack) + names(sta) <- c("theta", "sdboot", "z0", "a", "sdjack") + lims[, j] <- li + stats[, j] <- sta + } + Limsd[, k] <- apply(lims, 1, sd) * (J - 1) / sqrt(J) + Statsd[, k] <- apply(stats, 1, sd) * (J - 1) / sqrt(J) + } + limsd <- rowMeans(Limsd, 1) + statsd <- rowMeans(Statsd, 1) + limits <- cbind(vl0$lims[, 1], limsd, vl0$lims[, 2], pct) + dimnames(limits) <- list(alpha, c( + "bca", "jacksd", "std", + "pct" + )) + stats <- rbind(stats0, statsd) + dimnames(stats) <- list(c("est", "jsd"), c( + "theta", "sdboot", + "z0", "a", "sdjack" + )) + vl <- list( + call = call, lims = limits, stats = stats, B.mean = B.mean, + seed = seed + ) + if (ttind == 0) { + vl$ustats <- ustats + } + bcaboot.return(vl) +} + +bcaboot.return <- function(x) { + class(x) <- "bcaboot" + x +} + +#' @export +print.bcaboot <- function(x, digits = getOption("digits"), ...) { + result <- x + result$seed <- NULL + print.default(result) + invisible(x) +} diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 7108133..d7d935d 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -5,6 +5,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul yaxis = "P-value", color = "#000000", fill = "#239a98") { + Cairo.capabilities() # Consonance Function ----------------------------------------------------- @@ -70,7 +71,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = yaxis ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -171,7 +172,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "S-value \n(Bits of Information)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -224,7 +225,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Relative Likelihood \n(1/MLR)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -287,7 +288,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Log Likelihood" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -303,13 +304,13 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } @@ -350,7 +351,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Likelihood" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -366,13 +367,13 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } @@ -413,7 +414,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Deviance Statistic \n2ln(MLR)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), diff --git a/R/plot_compare.R b/R/plot_compare.R index 2e3ca77..ad14255 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -6,6 +6,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu color = "#000000", fill1 = "#239a98", fill2 = "#d46c5b") { + Cairo.capabilities() cols <- c(fill1, fill2) # Consonance Function ----------------------------------------------------- @@ -76,7 +77,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu data = data2, aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue, fill = fill2), alpha = 0.30 ) + - theme_bw() + + theme_hc() + labs( title = title, subtitle = subtitle, @@ -90,7 +91,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .998), + legend.position = c(.998, .95), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") @@ -216,7 +217,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "S-value (bits of information)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -224,7 +225,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .2), + legend.position = c(.998, .25), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") @@ -294,7 +295,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Relative Likelihood \n(1/MLR)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -302,7 +303,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .998), + legend.position = c(.998, .95), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") @@ -382,7 +383,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Log-Likelihood" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -390,7 +391,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .998), + legend.position = c(.998, .95), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") @@ -417,13 +418,13 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } @@ -470,7 +471,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Likelihood" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -478,7 +479,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .998), + legend.position = c(.998, .95), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") @@ -505,13 +506,13 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu if (nullvalue == TRUE) { if (measure == "default") { annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 0, xend = 0, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } else if (measure == "ratio") { annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .75, linetype = 1 + x = 1, xend = 1, y = 0, yend = 1, + color = "#990000", alpha = 0.3, size = .75, linetype = 1 ) } } @@ -557,7 +558,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Deviance Statistic \n2ln(MLR)" ) + - theme_bw() + + theme_hc() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -565,7 +566,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu axis.title.y = element_text(size = 12), text = element_text(size = 11), legend.background = element_blank(), - legend.position = c(.998, .2), + legend.position = c(.998, .35), legend.justification = c("right", "top"), legend.key = element_rect(linetype = 1), legend.key.size = unit(0.495, "cm") diff --git a/codemeta.json b/codemeta.json index f81a674..bb7ae3b 100644 --- a/codemeta.json +++ b/codemeta.json @@ -103,6 +103,30 @@ }, "sameAs": "https://CRAN.R-project.org/package=ggplot2" }, + { + "@type": "SoftwareApplication", + "identifier": "ggthemes", + "name": "ggthemes", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=ggthemes" + }, + { + "@type": "SoftwareApplication", + "identifier": "Cairo", + "name": "Cairo", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=Cairo" + }, { "@type": "SoftwareApplication", "identifier": "metafor", @@ -225,7 +249,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3841.184KB", + "fileSize": "3841.715KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/vitd.R b/vitd.R deleted file mode 100644 index caedb28..0000000 --- a/vitd.R +++ /dev/null @@ -1,55 +0,0 @@ -df1 <- curve_rev(point = 3.00, LL = 0.13, UL = 70.30, measure = "ratio", steps = 10000) -df2 <- curve_rev(point = 1.31, LL = 0.45, UL = 3.80, measure = "ratio", steps = 10000) -df3 <- curve_rev(point = 0.51, LL = 0.25, UL = 1.02, measure = "ratio", steps = 10000) -df4 <- curve_rev(point = 0.89, LL = 0.75, UL = 1.04, measure = "ratio", steps = 10000) -df5 <- curve_rev(point = 1.05, LL = 0.87, UL = 1.26, measure = "ratio", steps = 10000) -df6 <- curve_rev(point = 0.24, LL = 0.03, UL = 2.10, measure = "ratio", steps = 10000) -df7 <- curve_rev(point = 3.70, LL = 1.06, UL = 12.92, measure = "ratio", steps = 10000) -df8 <- curve_rev(point = 0.90, LL = 0.77, UL = 1.07, measure = "ratio", steps = 10000) -df9 <- curve_rev(point = 0.89, LL = 0.68, UL = 1.16, measure = "ratio", steps = 10000) -df10 <- curve_rev(point = 0.50, LL = 0.05, UL = 5.43, measure = "ratio", steps = 10000) -df11 <- curve_rev(point = 1.19, LL = 0.42, UL = 3.33, measure = "ratio", steps = 10000) -df12 <- curve_rev(point = 1.27, LL = 0.64, UL = 2.50, measure = "ratio", steps = 10000) -df13 <- curve_rev(point = 0.33, LL = 0.11, UL = 1.02, measure = "ratio", steps = 10000) -df14 <- curve_rev(point = 0.78, LL = 0.49, UL = 1.26, measure = "ratio", steps = 10000) -df15 <- curve_rev(point = 0.85, LL = 0.56, UL = 1.28, measure = "ratio", steps = 10000) -df16 <- curve_rev(point = 0.32, LL = 0.01, UL = 7.48, measure = "ratio", steps = 10000) -df17 <- curve_rev(point = 2.95, LL = 0.12, UL = 71.60, measure = "ratio", steps = 10000) -df18 <- curve_rev(point = 3.00, LL = 0.13, UL = 70.02, measure = "ratio", steps = 10000) -df19 <- curve_rev(point = 0.35, LL = 0.01, UL = 8.31, measure = "ratio", steps = 10000) -df20 <- curve_rev(point = 0.95, LL = 0.88, UL = 1.02, measure = "ratio", steps = 10000) -df21 <- curve_rev(point = 4.72, LL = 0.23, UL = 97.90, measure = "ratio", steps = 10000) -df22 <- curve_rev(point = 1.50, LL = 0.56, UL = 4.04, measure = "ratio", steps = 10000) -df23 <- curve_rev(point = 0.20, LL = 0.01, UL = 4.21, measure = "ratio", steps = 10000) -df24<- curve_rev(point = 0.33, LL = 0.01, UL = 8.06, measure = "ratio", steps = 10000) -df25 <- curve_rev(point = 0.09, LL = 0.00, UL = 2.08, measure = "ratio", steps = 10000) -df26 <- curve_rev(point = 0.37, LL = 0.02, UL = 8.66, measure = "ratio", steps = 10000) -df27 <- curve_rev(point = 1.25, LL = 0.59, UL = 2.66, measure = "ratio", steps = 10000) -df28 <- curve_rev(point = 2.90, LL = 0.60, UL = 14.09, measure = "ratio", steps = 10000) -df29 <- curve_rev(point = 1.00, LL = 0.14, UL = 7.07, measure = "ratio", steps = 10000) -df30 <- curve_rev(point = 0.48, LL = 0.04, UL = 5.20, measure = "ratio", steps = 10000) -df31 <- curve_rev(point = 2.93, LL = 0.12, UL = 71.47, measure = "ratio", steps = 10000) -df32 <- curve_rev(point = 0.50, LL = 0.05, UL = 5.46, measure = "ratio", steps = 10000) -df33 <- curve_rev(point = 1.00, LL = 0.06, UL = 15.89, measure = "ratio", steps = 10000) -df34 <- curve_rev(point = 1.12, LL = 0.79, UL = 1.58, measure = "ratio", steps = 10000) -df35 <- curve_rev(point = 1.09, LL = 0.73, UL = 1.65, measure = "ratio", steps = 10000) -df36 <- curve_rev(point = 4.91, LL = 0.58, UL = 41.71, measure = "ratio", steps = 10000) -df37 <- curve_rev(point = 0.32, LL = 0.01, UL = 7.79, measure = "ratio", steps = 10000) -df38 <- curve_rev(point = 0.07, LL = 0.00, UL = 1.36, measure = "ratio", steps = 10000) -df39 <- curve_rev(point = 1.01, LL = 0.49, UL = 2.11, measure = "ratio", steps = 10000) -df40 <- curve_rev(point = 0.99, LL = 0.87, UL = 1.11, measure = "ratio", steps = 10000) -df41 <- curve_rev(point = 0.33, LL = 0.01, UL = 8.11, measure = "ratio", steps = 10000) -df42 <- curve_rev(point = 0.95, LL = 0.90, UL = 1.00, measure = "ratio", steps = 10000) -df43 <- curve_rev(point = 1.00, LL = 0.42, UL = 2.41, measure = "ratio", steps = 10000) -df44 <- curve_rev(point = 0.34, LL = 0.01, UL = 8.16, measure = "ratio", steps = 10000) -df45 <- curve_rev(point = 1.57, LL = 0.55, UL = 4.47, measure = "ratio", steps = 10000) -df46 <- curve_rev(point = 1.20, LL = 1.04, UL = 1.37, measure = "ratio", steps = 10000) -df47 <- curve_rev(point = 1.00, LL = 0.87, UL = 1.15, measure = "ratio", steps = 10000) -df48 <- curve_rev(point = 0.99, LL = 0.93, UL = 1.05, measure = "ratio", steps = 10000) -df49 <- curve_rev(point = 0.63, LL = 0.13, UL = 3.07, measure = "ratio", steps = 10000) -df50 <- curve_rev(point = 0.20, LL = 0.01, UL = 4.14, measure = "ratio", steps = 10000) -df51 <- curve_rev(point = 0.33, LL = 0.01, UL = 8.12, measure = "ratio", steps = 10000) -df52 <- curve_rev(point = 1.96, LL = 0.38, UL = 10.26, measure = "ratio", steps = 10000) -df53 <- curve_rev(point = 1.03, LL = 1.09, UL = 70.30, measure = "ratio", steps = 10000) - - From 962b4803fced167d38e86bdbb84d575d2e5468ed Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Sat, 30 Nov 2019 04:42:58 -0500 Subject: [PATCH 08/21] curve_boot implemented and cleaned website documents --- .DS_Store | Bin 18436 -> 18436 bytes DESCRIPTION | 4 +- NAMESPACE | 4 +- R/curve_boot.R | 69 ++ R/curve_compare.R | 11 +- R/curve_corr.R | 8 +- R/curve_gen.R | 12 +- R/curve_lik.R | 10 +- R/curve_mean.R | 8 +- R/curve_meta.R | 6 +- R/curve_rev.R | 13 +- R/curve_sampling.R | 167 ----- R/curve_surv.R | 7 +- R/curve_table.R | 8 +- R/ggconcurve.R | 101 ++- R/plot_compare.R | 45 +- R/plot_concurve.R | 2 +- codemeta.json | 30 +- docs/404.html | 193 ------ docs/LICENSE-text.html | 867 ------------------------- docs/articles/examples.html | 324 --------- docs/articles/figures/function1.png | Bin 55806 -> 0 bytes docs/articles/figures/function2.png | Bin 55655 -> 0 bytes docs/articles/figures/function3.png | Bin 58415 -> 0 bytes docs/articles/figures/function4.png | Bin 57436 -> 0 bytes docs/articles/figures/function5.png | Bin 60338 -> 0 bytes docs/articles/figures/function6.png | Bin 60868 -> 0 bytes docs/articles/figures/graphmenu.png | Bin 762111 -> 0 bytes docs/articles/figures/likelihood.png | Bin 35645 -> 0 bytes docs/articles/figures/matrix.png | Bin 45799 -> 0 bytes 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docs/sitemap.xml diff --git a/.DS_Store b/.DS_Store index 77783fd69602f74fea41f7f813c57f3d506f9136..a322411cd0f98c9ec4f4cc1f78a7af5957613b12 100644 GIT binary patch delta 54 zcmZpfz}PZ@alXLhN<_$=Iv^} FiU1lt60QIM diff --git a/DESCRIPTION b/DESCRIPTION index 7b60753..82ce644 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,10 +14,10 @@ Authors@R: c( Maintainer: Zad R. Chow Description: Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) , Schweder T, Hjort NL. (2002) , Singh K, Xie M, Strawderman WE. (2007) , Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) , Greenland S. (2019) , Chow ZR, Greenland S. (2019) , and Greenland S, Chow ZR. (2019) . Imports: parallel, + pbapply, compiler, + bcaboot, ggplot2, - ggthemes, - Cairo, metafor, dplyr, tidyr, diff --git a/NAMESPACE b/NAMESPACE index 262f8d0..f65672e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,8 +1,8 @@ exportPattern("^[[:alpha:]]+") import(parallel) +import(pbapply) +import(bcaboot) import(ggplot2) -import(ggthemes) -import(Cairo) import(dplyr) import(tidyr) import(flextable) diff --git a/R/curve_boot.R b/R/curve_boot.R new file mode 100644 index 0000000..ba13b66 --- /dev/null +++ b/R/curve_boot.R @@ -0,0 +1,69 @@ +curve_boot <- function(data = data, func = func, replicates = 20000, steps = 1000) { + intrvls <- 0.5 / steps + alpha <- seq(0.00, 0.50, intrvls) + pboptions(type = "timer", style = 1, char = "+") + + result <- bcajack(x = data, B = replicates, func = func, alpha = alpha, verbose = FALSE) + + + z <- result[["lims"]] + z <- as.data.frame(z) + z <- as_tibble(rownames_to_column(z)) + colnames(z)[1] <- "alphaperc" + z$alphaperc <- as.numeric(z$alphaperc) + 1:length(alpha) + + # Data Frame with BCA Intervals ------------------------------ + pboptions(type = "timer", style = 1, char = "+") + bca <- pblapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), cl = detectCores() - 1) + bcaintervals <- data.frame(do.call(rbind, bca)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(bcaintervals) <- intrvl.limit + news <- pblapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), cl = detectCores() - 1) + width <- data.frame(do.call(rbind, news)) + colnames(width) <- "intrvl.width" + bews <- pblapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), cl = detectCores() - 1) + levels <- data.frame(do.call(rbind, bews)) + colnames(levels) <- "intrvl.level" + + df_bca <- data.frame(bcaintervals$lower.limit, bcaintervals$upper.limit, levels$intrvl.level, width$intrvl.width) + df_names <- c("lower.limit", "upper.limit", "intrvl.level", "intrvl.width") + colnames(df_bca) <- df_names + df_bca$pvalue <- 1 - df_bca$intrvl.level + df_bca$svalue <- -log2(df_bca$pvalue) + df_bca$cdf <- (abs(df_bca$intrvl.level / 2)) + 0.5 + df_bca <- head(df_bca, -1) + df_bca <- df_bca[-1, ] + + # Data Frame with Standard Intervals ------------------------------ + + std <- pblapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), cl = detectCores() - 1) + stdintervals <- data.frame(do.call(rbind, std)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(stdintervals) <- intrvl.limit + news <- pblapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), cl = detectCores() - 1) + width <- data.frame(do.call(rbind, news)) + colnames(width) <- "intrvl.width" + bews <- pblapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), cl = detectCores() - 1) + levels <- data.frame(do.call(rbind, bews)) + colnames(levels) <- "intrvl.level" + + df_std <- data.frame(stdintervals$lower.limit, stdintervals$upper.limit, levels$intrvl.level, width$intrvl.width) + df_names <- c("lower.limit", "upper.limit", "intrvl.level", "intrvl.width") + colnames(df_std) <- df_names + df_std$pvalue <- 1 - df_std$intrvl.level + df_std$svalue <- -log2(df_std$pvalue) + df_std$cdf <- (abs(df_std$intrvl.level / 2)) + 0.5 + df_std <- head(df_std, -1) + df_std <- df_std[-1, ] + + + # Combine Data Frames ----------------------------------------------------- + + dataframes <- list(df_std, df_bca) + names(dataframes) <- c("Standard", "BCA") + return(dataframes) +} + +# RMD Check ----------------------------------------------------- +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_compare.R b/R/curve_compare.R index 56eaea1..24f7b75 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -6,13 +6,13 @@ curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data1) != 6) { + if (ncol(data1) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.data.frame(data2) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (plot == TRUE) { @@ -71,13 +71,13 @@ curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data1) != 6) { + if (ncol(data1) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.data.frame(data2) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } @@ -132,3 +132,6 @@ curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { } } } + +# RMD Check +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_corr.R b/R/curve_corr.R index bd01cc9..4630035 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -9,18 +9,20 @@ curve_corr <- function(x, y, alternative, method, steps = 10000) { stop("Error: 'steps' must be a numeric vector") } + pboptions(type = "timer", style = 1, char = "+") intrvls <- (0:steps) / steps - results <- mclapply(intrvls, FUN = function(i) { + results <- pblapply(intrvls, FUN = function(i) { cor.test(x, y, alternative = alternative, method = method, exact = NULL, conf.level = i, continuity = FALSE )$conf.int[] - }, mc.cores = detectCores(logical = FALSE) - 1) + }, cl = detectCores() - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) df <- head(df, -1) @@ -28,4 +30,4 @@ curve_corr <- function(x, y, alternative, method, steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_gen.R b/R/curve_gen.R index 7709094..6c9f207 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -13,13 +13,14 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } + pboptions(type = "timer", style = 1, char = "+") intrvls <- (0:steps) / steps if (method == "default") { - results <- mclapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], cl = detectCores() - 1) } else if (method == "Wald") { - results <- mclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], cl = detectCores() - 1) } else if (method == "lm") { - results <- mclapply(intrvls, FUN = function(i) confint.lm(object = model, level = i)[var, ], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) confint.lm(object = model, level = i)[var, ], cl = detectCores() - 1) } else if (method == "boot") { effect <- coef(model)[[var]] boot_dist <- replicate(replicates, @@ -27,7 +28,7 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = data = model$model[sample(nrow(model$model), family = model$family$family)] ))[[var]] ) - effect - results <- mclapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores() - 1) + results <- pblapply(intrvls, FUN = function(i) effect - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores() - 1) } df <- data.frame(do.call(rbind, results)) @@ -35,6 +36,7 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = colnames(df) <- intrvl.limit df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) df <- head(df, -1) @@ -42,4 +44,4 @@ curve_gen <- function(model, var, method = "default", replicates = 1000, steps = } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_lik.R b/R/curve_lik.R index a79f2d9..4263d3d 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -11,14 +11,14 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'") } - + pboptions(type = "timer", style = 1, char = "+") intrvls <- (1:steps) / steps z <- qnorm(1 - intrvls / 2) if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores(logical = FALSE) - 1) - UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores(logical = FALSE) - 1) + LL <- pblapply(z, FUN = function(i) point + (i * se), cl = detectCores() - 1) + UL <- pblapply(z, FUN = function(i) point - (i * se), cl = detectCores() - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -27,8 +27,8 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores(logical = FALSE) - 1) - logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores(logical = FALSE) - 1) + logLL <- pblapply(z, FUN = function(i) logpoint + (i * se), cl = detectCores() - 1) + logUL <- pblapply(z, FUN = function(i) logpoint - (i * se), cl = detectCores() - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_mean.R b/R/curve_mean.R index 772d462..cf01e0c 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -16,9 +16,10 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } + pboptions(type = "timer", style = 1, char = "+") intrvls <- (0:steps) / steps if (method == "default") { - results <- mclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], cl = detectCores() - 1) } else if (method == "boot") { diff <- mean(x) - mean(y) if (paired) { @@ -32,13 +33,14 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = mean(sample(y, length(y), replace = T)) ) - diff } - results <- mclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), cl = detectCores() - 1) } df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) df <- head(df, -1) @@ -47,4 +49,4 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_meta.R b/R/curve_meta.R index 2b23e1e..b4a32fa 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -10,8 +10,9 @@ curve_meta <- function(x, measure = "default", steps = 10000) { if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } + pboptions(type = "timer", style = 1, char = "+") intrvls <- (0:steps) / steps - results <- mclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], cl = detectCores() - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -26,9 +27,10 @@ curve_meta <- function(x, measure = "default", steps = 10000) { df$upper.limit <- exp(df$upper.limit) } df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df <- head(df, -1) return(df) } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_rev.R b/R/curve_rev.R index 18049c5..d35bee4 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -13,14 +13,14 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'") } - + pboptions(type = "timer", style = 1, char = "+") intrvls <- (1:steps) / steps z <- qnorm(1 - intrvls / 2) if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- mclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores()) - UL <- mclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores()) + LL <- pblapply(z, FUN = function(i) point + (i * se), cl = detectCores() - 1) + UL <- pblapply(z, FUN = function(i) point - (i * se), cl = detectCores() - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -29,8 +29,8 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- mclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores()) - logUL <- mclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores()) + logLL <- pblapply(z, FUN = function(i) logpoint + (i * se), cl = detectCores() - 1) + logUL <- pblapply(z, FUN = function(i) logpoint - (i * se), cl = detectCores() - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -39,6 +39,7 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { } df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- 1 - intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df$pvalue <- 1 - (1 - intrvls) df$svalue <- -log2(df$pvalue) df <- head(df, -1) @@ -46,4 +47,4 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_sampling.R b/R/curve_sampling.R deleted file mode 100644 index 2c8a022..0000000 --- a/R/curve_sampling.R +++ /dev/null @@ -1,167 +0,0 @@ -curve_sampling <- function(x, B, func, ..., m = nrow(x), mr = 5, K = 2, J = 10, - alpha, verbose = TRUE) { - call <- match.call() - if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { - stats::runif(1) - } - seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) - if (is.vector(x)) { - x <- as.matrix(x) - } - n <- nrow(x) - nc <- ncol(x) - ttind <- ifelse(length(B) > 1, 1, 0) - if (ttind == 1) { - tt <- B - B <- length(tt) - } - else { - tt <- rep(0, B) - } - t0 <- func(x, ...) - u <- numeric(length = m) - m1 <- sqrt(m * (m - 1)) - if (m == n) { - for (i in seq_len(n)) { - u[i] <- func(x[-i, ], ...) - } - t. <- (mean(u) - u) * (m - 1) - a <- (1 / 6) * sum(t.^3) / (sum(t.^2))^1.5 - sdjack <- sqrt(sum(t.^2)) / m1 - } - if (m < n) { - aa <- ssj <- numeric(mr) - r <- n %% m - seq_len_m <- seq_len(m) - for (k in seq_len(mr)) { - Imat <- sapply(seq_len_m, sample.int, n = n, size = n - - r) - Iout <- setdiff(seq_len(n), Imat) - for (j in seq_len_m) { - Ij <- setdiff(seq_len_m, j) - ij <- c(c(Imat[Ij, ], Iout)) - u[j] <- func(x[ij, ]) - } - t. <- (mean(u) - u) * (m - 1) - aa[k] <- (1 / 6) * sum(t.^3) / (sum(t.^2))^1.5 - ssj[k] <- sqrt(sum(t.^2)) / m1 - } - a <- mean(aa) - sdjack <- mean(ssj) - } - if (ttind == 0) { - tY. <- Y. <- rep(0, n) - if (verbose) { - pb <- utils::txtProgressBar(min = 0, max = B, style = 3) - } - for (j in seq_len(B)) { - ij <- sample(x = n, size = n, replace = TRUE) - Yj <- table(c(ij, 1:n)) - 1 - tt[j] <- func(x[ij, ], ...) - tY. <- tY. + tt[j] * Yj - Y. <- Y. + Yj - if (verbose) { - utils::setTxtProgressBar(pb, j) - } - } - if (verbose) { - close(pb) - } - tt. <- mean(tt) - tY. <- tY. / B - Y. <- Y. / B - s. <- n * (tY. - tt. * Y.) - u. <- 2 * t. - s. - sdu <- sqrt(sum(u.^2)) / n - ustat <- 2 * t0 - tt. - ustats <- c(ustat, sdu) - names(ustats) <- c("ustat", "sdu") - } - B.mean <- c(B, mean(tt)) - alpha <- alpha[alpha < 0.5] - alpha <- c(alpha, 0.5, rev(1 - alpha)) - zalpha <- stats::qnorm(alpha) - nal <- length(alpha) - sdboot0 <- stats::sd(tt) - z00 <- stats::qnorm(sum(tt < t0) / B) - iles <- stats::pnorm(z00 + (z00 + zalpha) / (1 - a * (z00 + - zalpha))) - ooo <- trunc(iles * B) - ooo <- pmin(pmax(ooo, 1), B) - lims0 <- sort(tt)[ooo] - standard <- t0 + sdboot0 * stats::qnorm(alpha) - lims0 <- cbind(lims0, standard) - dimnames(lims0) <- list(alpha, c("bca", "std")) - stats0 <- c(t0, sdboot0, z00, a, sdjack) - names(stats0) <- c("theta", "sdboot", "z0", "a", "sdjack") - vl0 <- list( - lims = lims0, stats = stats0, B.mean = B.mean, - call = call, seed = seed - ) - if (K == 0) { - bcaboot.return(vl0) - } - pct <- rep(0, nal) - for (i in 1:nal) pct[i] <- sum(tt <= lims0[i, 1]) / B - Stand <- vl0$stats[1] + vl0$stats[2] * stats::qnorm(alpha) - Limsd <- matrix(0, length(alpha), K) - Statsd <- matrix(0, 5, K) - for (k in 1:K) { - II <- sample(x = B, size = B) - II <- matrix(II, ncol = J) - lims <- matrix(0, length(alpha), J) - stats <- matrix(0, 5, J) - for (j in 1:J) { - iij <- c(II[, -j]) - ttj <- tt[iij] - Bj <- length(ttj) - sdboot <- stats::sd(ttj) - z0 <- stats::qnorm(sum(ttj < t0) / Bj) - iles <- stats::pnorm(z0 + (z0 + zalpha) / (1 - a * - (z0 + zalpha))) - oo <- trunc(iles * Bj) - oo <- pmin(pmax(oo, 1), Bj) - li <- sort(ttj)[oo] - standard <- t0 + sdboot * stats::qnorm(alpha) - sta <- c(t0, sdboot, z0, a, sdjack) - names(sta) <- c("theta", "sdboot", "z0", "a", "sdjack") - lims[, j] <- li - stats[, j] <- sta - } - Limsd[, k] <- apply(lims, 1, sd) * (J - 1) / sqrt(J) - Statsd[, k] <- apply(stats, 1, sd) * (J - 1) / sqrt(J) - } - limsd <- rowMeans(Limsd, 1) - statsd <- rowMeans(Statsd, 1) - limits <- cbind(vl0$lims[, 1], limsd, vl0$lims[, 2], pct) - dimnames(limits) <- list(alpha, c( - "bca", "jacksd", "std", - "pct" - )) - stats <- rbind(stats0, statsd) - dimnames(stats) <- list(c("est", "jsd"), c( - "theta", "sdboot", - "z0", "a", "sdjack" - )) - vl <- list( - call = call, lims = limits, stats = stats, B.mean = B.mean, - seed = seed - ) - if (ttind == 0) { - vl$ustats <- ustats - } - bcaboot.return(vl) -} - -bcaboot.return <- function(x) { - class(x) <- "bcaboot" - x -} - -#' @export -print.bcaboot <- function(x, digits = getOption("digits"), ...) { - result <- x - result$seed <- NULL - print.default(result) - invisible(x) -} diff --git a/R/curve_surv.R b/R/curve_surv.R index 4e86dc0..efb563b 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -7,15 +7,16 @@ curve_surv <- function(data, x, steps = 10000) { if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - + pboptions(type = "timer", style = 1, char = "+") intrvls <- (1:steps) / steps - results <- mclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], mc.cores = detectCores(logical = FALSE) - 1) + results <- pblapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], cl = detectCores() - 1) df <- data.frame(do.call(rbind, results))[, 3:4] intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) df$intrvl.level <- intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 df$pvalue <- 1 - intrvls df$svalue <- -log2(df$pvalue) df <- head(df, -1) @@ -23,4 +24,4 @@ curve_surv <- function(data, x, steps = 10000) { } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/curve_table.R b/R/curve_table.R index b9040a5..4458c87 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,10 +1,10 @@ curve_table <- function(data, levels, type = "data.frame") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - - subdf <- mclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores(logical = FALSE) - 1) + pboptions(type = "timer", style = 1, char = "+") + subdf <- pblapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), cl = detectCores() - 1) subdf <- data.frame(do.call(rbind, subdf)) subdf$intrvl.level <- (subdf$intrvl.level * 100) - subcolnames <- c("Lower Limit", "Upper Limit", "Interval Width", "Interval Level (%)", "P-value", "S-value (bits)") + subcolnames <- c("Lower Limit", "Upper Limit", "Interval Width", "Interval Level (%)", "CDF", "P-value", "S-value (bits)") colnames(subdf) <- subcolnames subdf <- round(subdf, digits = 3) @@ -38,4 +38,4 @@ curve_table <- function(data, levels, type = "data.frame") { } } -utils::globalVariables(c("subdf", "Lower Limit", "Upper Limit", "Interval Width", "Interval Level", "P-value", "S-value")) +utils::globalVariables(c("subdf", "Lower Limit", "Upper Limit", "Interval Width", "Interval Level", "CDF", "P-value", "S-value")) diff --git a/R/ggconcurve.R b/R/ggconcurve.R index d7d935d..e733a20 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -5,15 +5,15 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul yaxis = "P-value", color = "#000000", fill = "#239a98") { - Cairo.capabilities() - # Consonance Function ----------------------------------------------------- + + # Consonance Curve ----------------------------------------------------- if (type == "c") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } - if (ncol(data) != 6) { + if (ncol(data) != 7) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -42,7 +42,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores(logical = FALSE) - 1) + interval <- pblapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), cl = detectCores() - 1) interval <- data.frame(do.call(rbind, interval)) interval <- pivot_longer(interval, X2:X3, names_to = "levels", values_to = "limits") interval <- interval[, -2] @@ -71,7 +71,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = yaxis ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -80,24 +80,24 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul text = element_text(size = 11) ) + { - if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) } + { - if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 5)) } + { if (position == "inverted") { scale_y_reverse( - breaks = seq(0, 1, .05), - sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + breaks = seq(0, 1, 0.10), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 10)) ) } } + { if (position == "pyramid") { scale_y_continuous( - breaks = seq(0, 1, .05), - sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + breaks = seq(0, 1, 0.10), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 10)) ) } } + @@ -117,12 +117,12 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul else if (nullvalue == FALSE) { } - # Surprisal Function ------------------------------------------------------ + # Surprisal Curve ------------------------------------------------------ } else if (type == "s") { if (is.data.frame(data) != TRUE) { stop("Error: 'data' must be a data frame from 'concurve'.") } - if (ncol(data) != 6) { + if (ncol(data) != 7) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -144,7 +144,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- mclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores(logical = FALSE) - 1) + interval <- pblapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), cl = detectCores() - 1) interval <- data.frame(do.call(rbind, interval)) interval <- gather(interval, key = "levels", value = "limits", X2:X3) interval <- interval[, -2] @@ -172,7 +172,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "S-value \n(Bits of Information)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -181,13 +181,68 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul text = element_text(size = 11) ) + { - if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) } + { - if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 5)) } + - scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) + scale_y_continuous(breaks = seq(0, 14, 1), expand = c(0, 0)) + + + + # Consonance Distribution ----------------------------------------------------- + } else if (type == "cdf") { + if (is.data.frame(data) != TRUE) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (ncol(data) != 7) { + stop("Error: 'data' must be a data frame from 'concurve'.") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'.") + } + if (is.logical(nullvalue) != TRUE) { + stop("Error: 'nullvalue' must be a logical statement such as 'TRUE' or 'FALSE'.") + } + if (is.character(position) != TRUE) { + stop("Error: 'position' must be a string such as 'pyramid' or 'inverted'.") + } + if (is.character(title) != TRUE) { + stop("Error: 'title' must be a string.") + } + if (is.character(subtitle) != TRUE) { + stop("Error: 'subtitle' must be a string.") + } + + if (is.character(yaxis) != TRUE) { + stop("Error: 'yaxis' must be a string.") + } + if (is.character(fill) != TRUE) { + stop("Error: 'fill' must be a string for the color.") + } + + values <- c(data$lower.limit, data$upper.limit) + ecdf(values) + if (measure == "default") { + plot(ecdf(values), + panel.first = grid(ny = 0), + xlab = xaxis, + ylab = "Distribution", + main = "Consonance Distribution", + las = 1 + ) + } else if (measure == "ratio") { + plot(ecdf(values), + panel.first = grid(ny = 0), + xlab = xaxis, + ylab = "Distribution", + main = "Consonance Distribution", + las = 1, + log = "x" + ) + } + abline(h = 0.5) # Relative Likelihood Function ----------------------------------------------------- } else if (type == "l1") { @@ -225,7 +280,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Relative Likelihood \n(1/MLR)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -288,7 +343,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Log Likelihood" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -351,7 +406,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Likelihood" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -414,7 +469,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul x = xaxis, y = "Deviance Statistic \n2ln(MLR)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 16), plot.subtitle = element_text(size = 12), @@ -444,4 +499,4 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul } # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "limit.ratio", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/plot_compare.R b/R/plot_compare.R index ad14255..fc298ed 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -6,22 +6,21 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu color = "#000000", fill1 = "#239a98", fill2 = "#d46c5b") { - Cairo.capabilities() cols <- c(fill1, fill2) - # Consonance Function ----------------------------------------------------- + # Consonance Curve ----------------------------------------------------- if (type == "c") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data1) != 6) { + if (ncol(data1) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.data.frame(data2) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -77,7 +76,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu data = data2, aes(x = upper.limit, ymin = min(pvalue), ymax = pvalue, fill = fill2), alpha = 0.30 ) + - theme_hc() + + theme_bw() + labs( title = title, subtitle = subtitle, @@ -112,24 +111,24 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu label.hjust = 4.5 )) + { - if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) } + { - if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 5)) } + { if (position == "inverted") { scale_y_reverse( - breaks = seq(0, 1, .05), - sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + breaks = seq(0, 1, 0.10), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 10)) ) } } + { if (position == "pyramid") { scale_y_continuous( - breaks = seq(0, 1, .05), - sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 5)) + breaks = seq(0, 1, 0.10), + sec.axis = sec_axis(~ (1 - .) * 100, name = "Levels for CI (%)", breaks = seq(0, 100, 10)) ) } } + @@ -150,18 +149,18 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu } - # Surprisal Function ------------------------------------------------------ + # Surprisal Curve ------------------------------------------------------ } else if (type == "s") { if (is.data.frame(data1) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data1) != 6) { + if (ncol(data1) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.data.frame(data2) != TRUE) { stop("Error: 'x' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 7) { stop("Error: 'x' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -217,7 +216,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "S-value (bits of information)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -246,12 +245,12 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu label.hjust = 4.5 )) + { - if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) + if (measure == "default") scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) } + { - if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) + if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 5)) } + - scale_y_continuous(breaks = seq(0, 14, 0.5), expand = c(0, 0)) + scale_y_continuous(breaks = seq(0, 14, 1.0), expand = c(0, 0)) # Relative Likelihood Function ----------------------------------------------------- @@ -295,7 +294,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Relative Likelihood \n(1/MLR)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -383,7 +382,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Log-Likelihood" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -471,7 +470,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Likelihood" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -558,7 +557,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu x = xaxis, y = "Deviance Statistic \n2ln(MLR)" ) + - theme_hc() + + theme_bw() + theme( plot.title = element_text(size = 12), plot.subtitle = element_text(size = 11), @@ -608,4 +607,4 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/R/plot_concurve.R b/R/plot_concurve.R index e083a9c..bff8da9 100644 --- a/R/plot_concurve.R +++ b/R/plot_concurve.R @@ -201,4 +201,4 @@ plot_concurve <- function(data, # RMD Check -utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "pvalue", "svalue")) +utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) diff --git a/codemeta.json b/codemeta.json index bb7ae3b..a7b3ee9 100644 --- a/codemeta.json +++ b/codemeta.json @@ -88,44 +88,44 @@ }, { "@type": "SoftwareApplication", - "identifier": "compiler", - "name": "compiler" - }, - { - "@type": "SoftwareApplication", - "identifier": "ggplot2", - "name": "ggplot2", + "identifier": "pbapply", + "name": "pbapply", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=ggplot2" + "sameAs": "https://CRAN.R-project.org/package=pbapply" }, { "@type": "SoftwareApplication", - "identifier": "ggthemes", - "name": "ggthemes", + "identifier": "compiler", + "name": "compiler" + }, + { + "@type": "SoftwareApplication", + "identifier": "bcaboot", + "name": "bcaboot", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=ggthemes" + "sameAs": "https://CRAN.R-project.org/package=bcaboot" }, { "@type": "SoftwareApplication", - "identifier": "Cairo", - "name": "Cairo", + "identifier": "ggplot2", + "name": "ggplot2", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=Cairo" + "sameAs": "https://CRAN.R-project.org/package=ggplot2" }, { "@type": "SoftwareApplication", @@ -249,7 +249,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3841.715KB", + "fileSize": "3841.12KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index abb4ea6..0000000 --- a/docs/404.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -Page not found (404) • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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    - - - - -
    - - - - - - - - - - - diff --git a/docs/articles/examples.html b/docs/articles/examples.html deleted file mode 100644 index ca289e2..0000000 --- a/docs/articles/examples.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - -Examples in R • concurve - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    -
    - - - - -
    -

    -Using Mean Differences

    -

    If we were to generate some random data from a normal distribution with the following code,

    - -

    and compare the means of these two “groups” using a t-test,

    -
    testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE)
    -

    we would likely see some differences, given that we have such a small sample in each group.

    -
    summary(testresults)
    -

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

    -

    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

    -

    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

    -

    It is relatively easy to produce such a function using the concurve package in R.

    -

    Install the package directly from CRAN

    -
    install.packages("concurve")
    -

    or get a more slightly up-to-date version via GitHub.

    -
    library(devtools)
    -install_github("zadchow/concurve")
    -

    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    - -

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    -
    tibble::tibble(intervalsdf)
    -

    There’s a quick look at the first few entries of our dataframe.

    -

    We can plot this data using the ggconcurve() function.

    -
    pfunction <- ggconcurve(type = "consonance", intervalsdf)
    -pfunction
    -
    - -
    -

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    -

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    -
    sfunction <- ggconcurve(type = "surprisal", intervalsdf)
    -sfunction
    -
    - -
    -

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    -
    -
    -

    -Simple Linear Models

    -

    We can also try this with other simple linear models.

    -

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    -
    GroupA2 <- rnorm(500)
    -GroupB2 <- rnorm(500)
    -
    -RandomData2 <- data.frame(GroupA2, GroupB2)
    -
    -model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    -
    -summary(model)
    -

    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    -
    randomframe <- curve_gen(model, "GroupB2")
    -tibble::tibble(randomframe)
    -

    Now that we have our data frame, we can graph our function.

    -
    ggconcurve(type = "consonance", randomframe)
    -
    - -
    -
    s <- ggconcurve(type = "surprisal", randomframe)
    -s
    -
    - -
    -

    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    - -

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    - -
    - -
    -

    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    -
    -
    -

    -Using Meta-Analysis Data

    -

    Here we present another quick example with a meta-analysis of simulated data.

    -

    First, we generate random data for two groups in two hypothetical studies

    -
    GroupAData <- runif(20, min = 0, max = 100)
    -GroupAMean <- round(mean(GroupAData), digits = 2)
    -GroupASD <- round(sd(GroupAData), digits = 2)
    -
    -GroupBData <- runif(20, min = 0, max = 100)
    -GroupBMean <- round(mean(GroupBData), digits = 2)
    -GroupBSD <- round(sd(GroupBData), digits = 2)
    -
    -GroupCData <- runif(20, min = 0, max = 100)
    -GroupCMean <- round(mean(GroupCData), digits = 2)
    -GroupCSD <- round(sd(GroupCData), digits = 2)
    -
    -GroupDData <- runif(20, min = 0, max = 100)
    -GroupDMean <- round(mean(GroupDData), digits = 2)
    -GroupDSD <- round(sd(GroupDData), digits = 2)
    -

    We can then quickly combine the data in a dataframe.

    -
    StudyName <- c("Study1", "Study2")
    -MeanTreatment <- c(GroupAMean, GroupCMean)
    -MeanControl <- c(GroupBMean, GroupDMean)
    -SDTreatment <- c(GroupASD, GroupCSD)
    -SDControl <- c(GroupBSD, GroupDSD)
    -NTreatment <- c(20, 20)
    -NControl <- c(20, 20)
    -
    -metadf <- data.frame(
    -  StudyName, MeanTreatment, MeanControl,
    -  SDTreatment, SDControl,
    -  NTreatment, NControl
    -)
    -

    Then, we’ll use metafor to calculate the standardized mean difference.

    - -

    Next, we’ll pool the data using a fixed-effects common-effects model

    - -

    Let’s look at our output.

    - -
    Fixed-Effects Model (k = 2)
    -
    -Test for Heterogeneity: 
    -Q(df = 1) = 0.61, p-val = 0.44
    -
    -Model Results:
    -
    -estimate    se  zval  pval  ci.lb  ci.ub 
    -    0.16  0.22  0.73  0.47  -0.28   0.60  
    -
    ----
    -Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    -

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    -

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    - -

    Now that we have our dataframe with every computed interval, we can plot the functions.

    -
    ggconcurve(type = "consonance", metaf)
    -
    - -
    -

    And our S-value function

    -
    ggconcurve(type = "surprisal", metaf)
    -
    - -
    -

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

    -
    -
    - - - -
    - - - - -
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z;A=bwyqaw4zb00XB(FuxB@C3UbuT^#DNLA8r!=nAopA3xAUb^v{u)G$;k(E>l3`bNz!geJ0FpIgpKg^QI*pR)5~c#-mCI zc;T?HmoLyG$8lv(?|cs$fQ$X|EtaYQ?&Z-$cl)V_wu<6PHXnGJ9U4{Q^74HN<+~}r z!oGd-3QBqxEoC)Mt?E>J8Frz<7t`Gtl=p;S2KlUd-ysNsx-A^Re3CZ)Ri(V5yxnvU zQVs;_SB^9NMHn4RtGyR5T+lc-S?t%KFyTz^zxGx)j+@IADjD8MK-kr(j!fNN zsGofbSsM&re&~HPbP{~^*%($cd#ZSMJhLG`S}N(_l7F{CTH(N{h-AH)mjvBlN@tdJ z9{vGcO;$*OmNni3X;hC+CDY1li;!R1l;AbjvbxWG1`%kbBx?Uyr=%Za7#Cg*FrX9S z`od<6+?Y=74=;!w0BnnIu*wpi9)YN&L8_yq@V4>7F`~V{ALAS-xk==$==XmZf0ygl z2YJ^q3tJUCvzTi#d9@h&2{YCi`Gv>)G-{P^M&M_vLyYiluPsK??A?eiJ7^8iJy^g4 z0(uWAeLq_)?9G#eC(KZv8_U#M1G06Wxw-lKax_}ULg>0t7&e>Rj1T@^`(}v{u#{yu__FR>C5nDc!pOrIfZzU`wbcXe=WJuHJ+Fzy?)H58pTcEXKrG7vdrjG G)IR~$U_#3P diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 91881a5..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,198 +0,0 @@ - - - - - - - - -Articles • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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    - - - - - - - - - - - diff --git a/docs/articles/stata.html b/docs/articles/stata.html deleted file mode 100644 index 37c5ef4..0000000 --- a/docs/articles/stata.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - -Using Stata • concurve - - - - - - - - - - - - - - - - - - - - -
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    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

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    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

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    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

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    First, let’s visualize the data with a scatterplot.

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    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

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    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

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    That’s what our output looks like.

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    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

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    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

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    Here’s the code that we’ll be using to achieve that.

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    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

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    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

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    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

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    • pvalue”is computed by taking 1 - “level”, which is alpha.

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    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

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    • my_new_data” is the filename that we’ve assigned to our new dataset.

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    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

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    Here are the next few major lines

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    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

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    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

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    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

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    The next line two lines

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    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

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    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

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    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

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    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

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    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

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    “post topost” is writing the results from each loop as new observations in this data structure.

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    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

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    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

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    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

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    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

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    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

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    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

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    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

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    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

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    So now, here’s what our consonance function looks like.

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    And here’s what our surprisal function looks like.

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    It looks slighly better when the y-axis is transformed logarithmically…

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    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

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    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

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    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

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    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

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    We simply replace the first line within the loop with our intended command, just as I’ve replaced

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    with

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    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

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    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

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    - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 382073a..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,219 +0,0 @@ - - - - - - - - -Citation and Authors • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - -

    Chow Z, Vigotsky A (2019). -concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. -R package version 2.2.0, https://CRAN.R-project.org/package=concurve. -

    -
    @Manual{,
    -  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
    -  author = {Zad R. Chow and Andrew D. Vigotsky},
    -  year = {2019},
    -  note = {R package version 2.2.0},
    -  url = {https://CRAN.R-project.org/package=concurve},
    -}
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    - -
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    - - - - - - - - - - - diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; 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- var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/docsearch.json b/docs/docsearch.json deleted file mode 100644 index aa1fc65..0000000 --- a/docs/docsearch.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "index_name": "lesslikely-concurve", - "start_urls": [ - { - "url": "https://data.lesslikely.com/concurve//index.html", - "selectors_key": "homepage", - "tags": [ - "homepage" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//reference", - "selectors_key": "reference", - "tags": [ - "reference" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//articles", - "selectors_key": "articles", - "tags": [ - "articles" - ] - } - ], - "stop_urls": [ - "/reference/$", - "/reference/index.html", - "/articles/$", - "/articles/index.html" - ], - "sitemap_urls": [ - "https://data.lesslikely.com/concurve//sitemap.xml" - ], - "selectors": { - "homepage": { - "lvl0": { - "selector": ".contents h1", - "default_value": "concurve Home page" - }, - "lvl1": { - "selector": ".contents h2" - }, - "lvl2": { - "selector": ".contents h3", - "default_value": "Context" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li, .contents .pre" - }, - "reference": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name", - "default_value": "Argument" - }, - "lvl2": { - "selector": ".ref-arguments th", - "default_value": "Description" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li" - }, - "articles": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name" - }, - "lvl2": { - "selector": ".contents h2, .contents h3", - "default_value": "Context" - }, - "text": ".contents p, .contents li, .tempate-article .contents .pre" - } - }, - "selectors_exclude": [ - ".dont-index" - ], - "min_indexed_level": 2, - "custom_settings": { - "separatorsToIndex": "_", - "attributesToRetrieve": [ - "hierarchy", - "content", - "anchor", - "url", - "url_without_anchor" - ] - } -} - diff --git a/docs/extra.css b/docs/extra.css deleted file mode 100644 index 0f1be09..0000000 --- a/docs/extra.css +++ /dev/null @@ -1,98 +0,0 @@ -html, body { - height: 100%; - font-size: 18px; - font-family: "PT Serif"; -} - -.navbar-brand { - font-size: 16px; - font-weight: bold; -} - -.navbar-default .navbar-nav>li>a:hover, -.navbar-default .navbar-nav>li>a:focus { - background-color: #eee; -} - -.nav-pills>li.active>a, -.nav-pills>li.active>a:hover, -.nav-pills>li.active>a:focus { - background-color:#d46c5b; -} - -.label-default { - background-color:#d46c5b; -} - -p.caption { - display: none; -} - -body { - color: #444444; - line-height: 1.6; -} - -a { - color: #d46c5b; -} - -a:focus { - color: #d46c5b; -} - -h1, .h1 { - font-size: 32px; -} - -h1 small, .h1 small { - font-size: 70%; - text-transform: uppercase; - letter-spacing: 0.5px; -} - -h2, .h2 { - font-size: 28px; -} - -h3, .h3 { - font-size: 20px; -} - -h4, .h4 { - font-size: 20px; -} - -.contents h1, .contents h2, .contents h3, .contents h4 { - margin-bottom: 20px; -} - -.contents .page-header h1 { - margin-top: 25px; -} - -.contents h3, .contents h4 { - margin-top: -55px; -} - -h4.date, h4.author { - font-size: 1em; - color: #bbb; - display: inline; - padding-right: 0.5em; -} - -pre code { - font-size: 13.5px; - white-space: pre; -} - -.btn-primary { - color: #eeeeee; - background-color: #d46c5b; -} - -.btn-primary:focus { - color: #eeeeee; - background-color: #d46c5b; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 9e74e9b..0000000 --- a/docs/index.html +++ /dev/null @@ -1,335 +0,0 @@ - - - - - - - -Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions • concurve - - - - - - - - - - - - - - - - - - - - -
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    -Examples

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    -Plot Publication-Ready Interval Functions

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    -(Consonance, Surprisal, Likelihood, & Deviance)

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    -Compare Interval Functions From Different Data/Studies

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    -Export Tabled Results Easily For Word, Powerpoint, & LaTeX

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    -Installation

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    -For R:

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    -Install the Package From CRAN

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    install.packages("concurve")
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    -Install the Developer Version

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    library(devtools)
    -install_github("zadchow/concurve")
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    -Check out the Examples.

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    -For Stata:

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    -Check out the Article on Using Stata for concurve.

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    -Dependencies

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    • ggplot2
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    • metafor
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    • parallel
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    -Purpose

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    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

    -

    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

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    Following the common (and important) warning that P-values are not hypothesis probabilities, we caution that the P-value graph is not a probability distribution: It shows compatibility of parameter values with the data, rather than plausibility or probability of those values given the data. This is not a subtle difference: compatibility is a much weaker condition than plausibility. Consider for example that complete fabrication of the data is always an explanation compatible with the data (and indeed has happened in some influential medical studies [65]), but in studies with many participants and authors involved in all aspects of data collection it becomes so implausible or improbable as to not even merit mention. We emphasize then that all the P-value ever addresses in a direct logical sense is compatibility; for hypothesis probabilities one must turn to Bayesian methods [25].

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    In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

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    From a scientific perspective, the likelihood function and p-value function provide the basis for scientific judgments by an investigator, and by other investigators who might have interest. It thus replaces a blunt yes or no decision by an opportunity for appropriate informed judgment.” - Fraser, 2019

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    Statistical software does not help you know what to compute, nor how to interpret the result. It does not offer to explain the assumptions behind methods, nor does it flag delicate or dubious assumptions. It does not warn you about multiplicity or p-hacking. It does not check whether you picked the hypothesis or analysis after looking at the data, nor track the number of analyses you tried before arriving at the one you sought to publish – another form of multiplicity. The more “powerful” and “user-friendly” the software is, the more it invites cargo-cult statistics." - Stark & Saltelli, 2018

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    -References

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    1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:1909.08579 [stat.ME]. 2019
    2. -
    3. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics. arXiv:1909.08583 [stat.ME]. 2019
    4. -
    5. Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.
    6. -
    7. Sullivan KM, Foster DA. Use of the confidence interval function._Epidemiology._ 1990;1(1):39-42.
    8. -
    9. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012.
    10. -
    11. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv [mathST]. 2007.
    12. -
    13. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332.
    14. -
    15. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There is No Replication Crisis if We Don’t Expect Replication. Am Stat. 2019
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    17. Greenland S. Valid P-values Behave Exactly As They Should. Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2019;18(136).
    18. -
    19. Fraser DAS. The p-value Function and Statistical Inference. Am Stat. 2019
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    21. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15(4):40-43.
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    - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/logo.svg b/docs/logo.svg deleted file mode 100644 index f42ed9b..0000000 --- a/docs/logo.svg +++ /dev/null @@ -1,93 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 854ed0f..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - -Changelog • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    -concurve 2.2.0 Unreleased -

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    -concurve 2.1.0 2019-09-19 -

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    -Major changes

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    • -ggconcurve() now plots both the P-values and CI level using both y-axes when the type = “consonance”. Previously, this was only possible via plot_concurve() (which uses base R graphics) because ggplot2 had a bug in its last few versions, which inhibited proper transformations in the y-axis.
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    -concurve 2.0.1 Unreleased -

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    • -plot_concurve() now has “measure” as an item which allows for ratio measures to be logarithmically scaled on the x axis. There are two options, “default”, which is set as the default option and is for mean differences, and “ratio”, which will result in the axis being logarithmically scaled.
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    -concurve 2.0 2019-07-10 -

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    • The plotpint() function which plotted consonance functions has been repackaged into ggconcurve().
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    • The plotsint() function which plotted surprisal functions has been repackaged into ggconcurve().
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    • Functions can now also be plotted with base R via the plot_concurve() function.
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    • Consonance functions can be plotted as a pyramid (right side up) or inverted (upside down) via the “position” item in ggconcurve().
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    • Null values (for means & ratios) can be plotted via the ggconcurve() function to show how much of the interval surrounds it.
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    -concurve 1.08 Unreleased -

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    -concurve 1.07 Unreleased -

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- - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('body').scrollspy({ - target: '#sidebar', - offset: 60 - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
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  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index c4bf0d4..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,10 +0,0 @@ -pandoc: '2.6' -pkgdown: 1.4.1 -pkgdown_sha: ~ -articles: - examples: examples.html - stata: stata.html -urls: - reference: https://data.lesslikely.com/concurve//reference - article: https://data.lesslikely.com/concurve//articles - diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html deleted file mode 100644 index bef7a09..0000000 --- a/docs/reference/curve_compare.html +++ /dev/null @@ -1,260 +0,0 @@ - - - - - - - - -Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets — curve_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    -
    - -
    curve_compare(data1, data2, type = "consonance", plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    data1

    data2

    type

    plot

    ...

    - -

    Details

    - -

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    Value

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    - - - -

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    References

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    Note

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    See also

    - -

    - -

    Examples

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    - -
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    - - - - - - - - - - - diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html deleted file mode 100644 index a8d68ef..0000000 --- a/docs/reference/curve_corr.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - - - - -Computes consonance intervals for correlations — curve_corr • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Computes consonance intervals to produce P- and S-value functions for correlational analyses -using the cor.test function in base R and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values.

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    - -
    curve_corr(x, y, alternative, method, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    x

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    y

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    alternative

    Indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.

    method

    A character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -joe <- curve_corr(x = GroupA, y = GroupB, - alternative = "two.sided", method = "pearson") - -tibble::tibble(joe)
    #> # A tibble: 10,000 x 1 -#> joe$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.398 0.398 0 0 1 0 -#> 2 0.398 0.398 0.0000308 0.0001 1.000 0.000144 -#> 3 0.398 0.398 0.0000616 0.0002 1.000 0.000289 -#> 4 0.398 0.398 0.0000923 0.000300 1.000 0.000433 -#> 5 0.398 0.398 0.000123 0.0004 1.000 0.000577 -#> 6 0.398 0.398 0.000154 0.0005 1.000 0.000722 -#> 7 0.398 0.398 0.000185 0.000600 0.999 0.000866 -#> 8 0.398 0.398 0.000215 0.0007 0.999 0.00101 -#> 9 0.398 0.398 0.000246 0.0008 0.999 0.00115 -#> 10 0.398 0.398 0.000277 0.0009 0.999 0.00130 -#> # … with 9,990 more rows
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    - - - - - - - - - - - diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html deleted file mode 100644 index da8608d..0000000 --- a/docs/reference/curve_gen.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - -Computes consonance intervals for linear models — curve_gen • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits -for each interval level into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_gen(model, var, method = "default", replicates = 1000, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here.

    var

    The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.

    method

    Chooses the method to be used to calculate the consonance intervals. There are currently four -methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to -compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically -what most people are familiar with when computing intervals based on the calculated standard error. -The "lm" method allows this function to be used for specific scenarios like logistic regression and -the 'glm' function. The "boot" method allows for bootstrapping at certain levels.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 compatibility intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data - -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -RandomData <- data.frame(GroupA, GroupB) - -rob <- glm(GroupA ~ GroupB, data = RandomData) -bob <- curve_gen(rob, "GroupB", method = "lm") - -tibble::tibble(bob)
    #> # A tibble: 10,000 x 1 -#> bob$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.0732 -0.0732 0 0 1 0 -#> 2 -0.0732 -0.0731 0.0000334 0.0001 1.000 0.000144 -#> 3 -0.0732 -0.0731 0.0000668 0.0002 1.000 0.000289 -#> 4 -0.0732 -0.0731 0.000100 0.000300 1.000 0.000433 -#> 5 -0.0732 -0.0731 0.000134 0.0004 1.000 0.000577 -#> 6 -0.0732 -0.0731 0.000167 0.0005 1.000 0.000722 -#> 7 -0.0733 -0.0731 0.000200 0.000600 0.999 0.000866 -#> 8 -0.0733 -0.0730 0.000234 0.0007 0.999 0.00101 -#> 9 -0.0733 -0.0730 0.000267 0.0008 0.999 0.00115 -#> 10 -0.0733 -0.0730 0.000301 0.0009 0.999 0.00130 -#> # … with 9,990 more rows
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_lik-1.png b/docs/reference/curve_lik-1.png deleted file mode 100644 index b9050d826ba2dd5dfd7fd62967c6de94f2b672af..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119953 zcmeFZXH-+!8#f9lpdg~s6r?yx7f?DV1Vur5FH)5vHS`*~fFOvdGy&3YMnFJ7A@}5w zIspN(2LZtulXE2C6Lf6aIsw7iYgW?Isy5QH()PCYP8tr+P0eM^?aZC5Ox0x{5)g<+ zL}(dWUe}_QtZ}xud8=E)RxsEl7ec{G5v47dR)+8!9~ZWZl@lKQW6^A9wfp0bZc%}n zqcoMrs@``oiPS9(mHsRoI7+l+8D9(;>nM_iL$YiyP<1gp8ykO5Al?d5GGV^zy_K|< z?6q2Lu|~XDov$1Fo|=$0p_qq`f5tf^GE{>5T~J6W9Z8yw8Y8!1o+6R33b}i^{-kuX zm+{Ep_;E^6r3@ntizBm1>3lAEXe8l!j#h^oA?)BiGX$f@v}3YZc~`214^}m8iu&p- z=^J77;^*y|fly*C4eQXjOOc(edomaBcRvpqrz%-v^B3pRO%h$lQ%fAyo*$<`n=FI3 zpkH%(;!`Ap)}9;SxrJC4rqx;$@;{`=9G;u8f%;TjpF+nbzP}71c{etZ0MEX>_Q;?R zO`2byl37$9cEfieNO*Px->e}td$C910-XqwS%DUN5TKrV@~AZ2$shidq(JRWgvuySH3qw{ z*4jcFnB(J}kUBCY-7}ZXIHX7>X@@QI0xy#AQx{QR3o}l8LC3Jr zcz;v8EWWF$zB&5%`*-8O{i^OVF0&BHm$YB#58oEj4w!Eg1RI!AL40qE5JA|kv0NDm 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ae36f7b..0000000 --- a/docs/reference/curve_lik.html +++ /dev/null @@ -1,300 +0,0 @@ - - - - - - - - -Computes Likelihood Intervals for Ratio Data -<!-- %% ~~function to do ... ~~ --> — curve_lik • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    curve_lik(point, LL, UL, measure = "ratio", steps = 10000)
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    Arguments

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    point

    LL

    UL

    measure

    steps

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    Details

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    Value

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    References

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    See also

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    - -

    Examples

    -
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) - -tibble::tibble(lik1)
    #> # A tibble: 327 x 1 -#> lik1$values $zscore $support $deviancestat -#> <dbl> <dbl> <dbl> <dbl> -#> 1 0.724 -3.89 0.000517 15.1 -#> 2 0.734 -3.83 0.000658 14.7 -#> 3 0.744 -3.77 0.000831 14.2 -#> 4 0.754 -3.71 0.00104 13.7 -#> 5 0.764 -3.65 0.00130 13.3 -#> 6 0.774 -3.59 0.00161 12.9 -#> 7 0.784 -3.53 0.00198 12.4 -#> 8 0.794 -3.47 0.00243 12.0 -#> 9 0.804 -3.41 0.00296 11.6 -#> 10 0.814 -3.36 0.00358 11.3 -#> # … with 317 more rows
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) - -tibble::tibble(lik2)
    #> # A tibble: 353 x 1 -#> lik2$values $zscore $support $deviancestat -#> <dbl> <dbl> <dbl> <dbl> -#> 1 0.624 -3.89 0.000517 15.1 -#> 2 0.634 -3.83 0.000664 14.6 -#> 3 0.644 -3.76 0.000848 14.1 -#> 4 0.654 -3.70 0.00107 13.7 -#> 5 0.664 -3.64 0.00135 13.2 -#> 6 0.674 -3.57 0.00168 12.8 -#> 7 0.684 -3.51 0.00208 12.3 -#> 8 0.694 -3.45 0.00257 11.9 -#> 9 0.704 -3.40 0.00314 11.5 -#> 10 0.714 -3.34 0.00381 11.1 -#> # … with 343 more rows
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
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    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html deleted file mode 100644 index 516a52e..0000000 --- a/docs/reference/curve_mean.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - - -Computes consonance intervals for mean differences — curve_mean • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
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    Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test -that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_mean(x, y, data, paired = F, method = "default",
    -replicates = 1000, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    Variable that contains the data for the first group being compared.

    y

    Variable that contains the data for the second group being compared.

    data

    Data frame from which the variables are being extracted from.

    paired

    Indicates whether the statistical test is a paired difference test. By default, it is set to "F", -which means the function will be an unpaired statistical test comparing two independent groups. -Inserting "paired" will change the test to a paired difference test.

    method

    By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted -into the function call.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data - -GroupA <- runif(100, min = 0, max = 100) -GroupB <- runif(100, min = 0, max = 100) - -RandomData <- data.frame(GroupA, GroupB) - -bob <- curve_mean(GroupA, GroupB, RandomData) - -tibble::tibble(bob)
    #> # A tibble: 10,000 x 1 -#> bob$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.919 -0.919 0 0 1 0 -#> 2 -0.919 -0.918 0.00104 0.0001 1.000 0.000144 -#> 3 -0.920 -0.917 0.00208 0.0002 1.000 0.000289 -#> 4 -0.920 -0.917 0.00313 0.000300 1.000 0.000433 -#> 5 -0.921 -0.916 0.00417 0.0004 1.000 0.000577 -#> 6 -0.921 -0.916 0.00521 0.0005 1.000 0.000722 -#> 7 -0.922 -0.915 0.00625 0.000600 0.999 0.000866 -#> 8 -0.922 -0.915 0.00729 0.0007 0.999 0.00101 -#> 9 -0.923 -0.914 0.00834 0.0008 0.999 0.00115 -#> 10 -0.923 -0.914 0.00938 0.0009 0.999 0.00130 -#> # … with 9,990 more rows
    -
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    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html deleted file mode 100644 index 25cf577..0000000 --- a/docs/reference/curve_meta.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - -Computes consonance intervals for meta-analysis data — curve_meta • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
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    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -meta-analysis done by the metafor package and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_meta(x, measure = "default", steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    x

    Object where the meta-analysis parameters are stored, typically a list produced by 'metafor'

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default. -If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3). -https://www.jstatsoft.org/article/view/v036i03/v36i03.pdf.

    -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data for two groups in two studies - -GroupAData <- runif(20, min = 0, max = 100) -GroupAMean <- round(mean(GroupAData), digits = 2) -GroupASD <- round(sd(GroupAData), digits = 2) - -GroupBData <- runif(20, min = 0, max = 100) -GroupBMean <- round(mean(GroupBData), digits = 2) -GroupBSD <- round(sd(GroupBData), digits = 2) - -GroupCData <- runif(20, min = 0, max = 100) -GroupCMean <- round(mean(GroupCData), digits = 2) -GroupCSD <- round(sd(GroupCData), digits = 2) - -GroupDData <- runif(20, min = 0, max = 100) -GroupDMean <- round(mean(GroupDData), digits = 2) -GroupDSD <- round(sd(GroupDData), digits = 2) - -# Combine the data - -StudyName <- c("Study1", "Study2") -MeanTreatment <- c(GroupAMean, GroupCMean) -MeanControl <- c(GroupBMean, GroupDMean) -SDTreatment <- c(GroupASD, GroupCSD) -SDControl <- c(GroupBSD, GroupDSD) -NTreatment <- c(20, 20) -NControl <- c(20, 20) - -metadf <- data.frame( - StudyName, MeanTreatment, MeanControl, - SDTreatment, SDControl, NTreatment, NControl -) - -# Use metafor to calculate the standardized mean difference - -library(metafor)
    #> Loading required package: Matrix
    #> Loading 'metafor' package (version 2.1-0). For an overview -#> and introduction to the package please type: help(metafor).
    -dat <- escalc( - measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, - m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf -) - -# Pool the data using a particular method. Here "FE" is the fixed-effects model - -res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) - -# Calculate the intervals using the metainterval function - -metaf <- curve_meta(res) - -tibble::tibble(metaf)
    #> # A tibble: 10,000 x 1 -#> metaf$lower.limit $upper.limit $intrvl.level $pvalue $svalue $intrvl.width -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.120 0.120 0 1 0 0 -#> 2 0.120 0.121 0.0001 1.000 0.000144 0.0000564 -#> 3 0.120 0.121 0.0002 1.000 0.000289 0.000113 -#> 4 0.120 0.121 0.000300 1.000 0.000433 0.000169 -#> 5 0.120 0.121 0.0004 1.000 0.000577 0.000226 -#> 6 0.120 0.121 0.0005 1.000 0.000722 0.000282 -#> 7 0.120 0.121 0.000600 0.999 0.000866 0.000339 -#> 8 0.120 0.121 0.0007 0.999 0.00101 0.000395 -#> 9 0.120 0.121 0.0008 0.999 0.00115 0.000451 -#> 10 0.120 0.121 0.0009 0.999 0.00130 0.000508 -#> # … with 9,990 more rows
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html deleted file mode 100644 index a829ed7..0000000 --- a/docs/reference/curve_rev.html +++ /dev/null @@ -1,269 +0,0 @@ - - - - - - - - -Reverse engineer consonance and surprisal functions from confidence limits and point estimates — curve_rev • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
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    Using the confidence limits and point estimates from a dataset, one can use these estimates to -compute thousands of consonance intervals and graph the intervals to form a consonance and -surprisal function.

    -
    - -
    curve_rev(point, LL, UL, measure = "default", steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    point

    The point estimate from an analysis. Ex: 1.20

    LL

    The lower confidence limit from an analysis Ex: 1.0

    UL

    The upper confidence limit from an analysis Ex: 1.4

    measure

    The type of data being used. If they involve mean differences, -then the "default" option should be used, which is also the default setting. -If the data are ratios, then the "ratio" option should be used.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# From a real published study. Point estimate of the result was hazard ratio of 1.61 and -# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. - -df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") - -tibble::tibble(df)
    #> # A tibble: 9,999 x 1 -#> df$lower.limit $upper.limit $intrvl.width $intrvl.level $pvalue $svalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.624 4.15 3.53 1.000 0.0001000 13.3 -#> 2 0.651 3.98 3.33 1.000 0.000200 12.3 -#> 3 0.667 3.88 3.22 1.000 0.000300 11.7 -#> 4 0.680 3.81 3.13 1.000 0.000400 11.3 -#> 5 0.690 3.76 3.07 1.000 0.000500 11.0 -#> 6 0.698 3.71 3.02 0.999 0.0006 10.7 -#> 7 0.705 3.68 2.97 0.999 0.0007 10.5 -#> 8 0.712 3.64 2.93 0.999 0.0008 10.3 -#> 9 0.717 3.61 2.90 0.999 0.0009 10.1 -#> 10 0.722 3.59 2.87 0.999 0.001 9.97 -#> # … with 9,989 more rows
    -
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    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html deleted file mode 100644 index 940f91e..0000000 --- a/docs/reference/curve_surv.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Produce Consonance Intervals for Survival Data — curve_surv • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - - -
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    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -Cox model computed by the 'survival' package and places the interval limits for each interval level -into a data frame along with the corresponding p-value and s-value.

    -
    - -
    curve_surv(data, x, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    Object where the Cox model is stored, typically a list produced by the 'survival' package.

    x

    Predictor of interest within the survival model for which the consonance intervals should be computed.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -
    - -
    - - - -
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z*{TES;ikzoUKw@YUE}}x>Kw2Szvl}_oePo6GXD>^`d^T4$8Ju9i%aiLLzdo^M zt_MKqsTx#f=>Bp59mEP}CwW0c_kVvj1n0&>ByXeXBIe%?2!I&<%uJPkz5vAbnGmeJ zNhqZFmjimi2{Mq&CjWGD@S5}z1nRn`G|K$#fPa_3zf0i%GrNFOc8fj=%I*6~vNHeN zH1h9`@b8ZB??d6=hr+*c!M}0AzoFs3q2a&T!N1wTzX{^M3F5ysf`4lS|CWjVe_bZJ ZkkUpDwI;iDq@aL5N^ - - - - - - - -Produces a Table of Relevant Statistics from the `concurve` functions -<!-- %% ~~function to do ... ~~ --> — curve_table • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Examples

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    -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - -(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Lower Limit Upper Limit Interval Width Interval Level (%) P-value -#> 2501 0.043 0.083 0.040 25.0 0.750 -#> 5001 0.021 0.105 0.084 50.0 0.500 -#> 7501 -0.008 0.135 0.143 75.0 0.250 -#> 8001 -0.016 0.143 0.159 80.0 0.200 -#> 8501 -0.026 0.153 0.179 85.0 0.150 -#> 9001 -0.039 0.166 0.205 90.0 0.100 -#> 9501 -0.059 0.185 0.244 95.0 0.050 -#> 9751 -0.076 0.203 0.279 97.5 0.025 -#> 9901 -0.097 0.224 0.321 99.0 0.010 -#> S-value (bits) -#> 2501 0.415 -#> 5001 1.000 -#> 7501 2.000 -#> 8001 2.322 -#> 8501 2.737 -#> 9001 3.322 -#> 9501 4.322 -#> 9751 5.322 -#> 9901 6.644
    (z <- curve_table(intervalsdf, type = "tibble"))
    #> # A tibble: 9 x 1 -#> subdf$`Lower Li… $`Upper Limit` $`Interval Widt… $`Interval Leve… $`P-value` -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.043 0.083 0.04 25 0.75 -#> 2 0.021 0.105 0.084 50 0.5 -#> 3 -0.008 0.135 0.143 75 0.25 -#> 4 -0.016 0.143 0.159 80 0.2 -#> 5 -0.026 0.153 0.179 85 0.15 -#> 6 -0.039 0.166 0.205 90 0.1 -#> 7 -0.059 0.185 0.244 95 0.05 -#> 8 -0.076 0.203 0.279 97.5 0.025 -#> 9 -0.097 0.224 0.321 99 0.01 -#> # … with 1 more variable: $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf, type = "latex"))
    #> -#> -#> | | Lower Limit| Upper Limit| Interval Width| Interval Level (%)| P-value| S-value (bits)| -#> |:----|-----------:|-----------:|--------------:|------------------:|-------:|--------------:| -#> |2501 | 0.043| 0.083| 0.040| 25.0| 0.750| 0.415| -#> |5001 | 0.021| 0.105| 0.084| 50.0| 0.500| 1.000| -#> |7501 | -0.008| 0.135| 0.143| 75.0| 0.250| 2.000| -#> |8001 | -0.016| 0.143| 0.159| 80.0| 0.200| 2.322| -#> |8501 | -0.026| 0.153| 0.179| 85.0| 0.150| 2.737| -#> |9001 | -0.039| 0.166| 0.205| 90.0| 0.100| 3.322| -#> |9501 | -0.059| 0.185| 0.244| 95.0| 0.050| 4.322| -#> |9751 | -0.076| 0.203| 0.279| 97.5| 0.025| 5.322| -#> |9901 | -0.097| 0.224| 0.321| 99.0| 0.010| 6.644|
    (z <- curve_table(intervalsdf, type = "image"))
    #> a flextable object. -#> col_keys: `Lower Limit`, `Upper Limit`, `Interval Width`, `Interval Level (%)`, `P-value`, `S-value (bits)` -#> header has 1 row(s) -#> body has 9 row(s) -#> original dataset sample: -#> Lower Limit Upper Limit Interval Width Interval Level (%) P-value -#> 2501 0.043 0.083 0.040 25 0.75 -#> 5001 0.021 0.105 0.084 50 0.50 -#> 7501 -0.008 0.135 0.143 75 0.25 -#> 8001 -0.016 0.143 0.159 80 0.20 -#> 8501 -0.026 0.153 0.179 85 0.15 -#> S-value (bits) -#> 2501 0.415 -#> 5001 1.000 -#> 7501 2.000 -#> 8001 2.322 -#> 8501 2.737
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    - - - - - - - - - - - diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html deleted file mode 100644 index 6e866dd..0000000 --- a/docs/reference/defunct.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -concurve. — defunct" /> - - - - - - - - - - - - - - - - - - -
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    The functions listed below are deprecated. Please use the listed alternatives.

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    -plotsint(...)
    -
    -meanintervals(...)
    -
    -metaintervals(...)
    -
    -genintervals(...)
    -
    -corrintervals(...)
    -
    -survintervals(...)
    -
    -rev_eng(...)
    - - -

    plotpint

    - - - -

    For plotpint, use ggconcurve or plot_concurve.

    -

    plotsint

    - - - -

    For plotsint, use ggconcurve or plot_concurve.

    -

    meanintervals

    - - - -

    For meanintervals, use curve_mean.

    -

    metaintervals

    - - - -

    For metaintervals, use curve_meta.

    -

    genintervals

    - - - -

    For genintervals, use curve_gen.

    -

    corrintervals

    - - - -

    For corrintervals, use curve_corr.

    -

    survintervals

    - - - -

    For survintervals, use curve_surv.

    -

    rev_eng

    - - - -

    For rev_eng, use curve_rev.

    - -
    - -
    - - - -
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a/docs/reference/ggconcurve.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - - -Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2 — ggconcurve • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

    -
    - - - - -
    - -
    -
    - - -
    -

    Takes the dataframe produced by the interval functions and plots the p-values/s-values, consonance (confidence) -levels, and the interval estimates to produce a p-value/s-value function using ggplot2 graphics.

    -
    - -
    ggconcurve(type, data, measure, nullvalue, position,
    -           title, subtitle, xaxis, yaxis, color, fill)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example ggconcurve(type = "surprisal") or ggconcurve(type = "consonance").

    data

    The dataframe produced by one of the interval functions in which the intervals are stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    nullvalue

    Indicates whether the null value for the measure should be plotted. By default, it is set to "absent", meaning it will not be plotted as a vertical line. Changing this to "present", will plot a vertical line at 0 when the measure is set to "default" and a vertical line at 1 when the measure is set to "ratio". For example, ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function By default, it is set to "pyramid", meaning the p-value function will stand right side up, like a pyramid. However, it can also be inverted via the option "inverted". This will also change the sequence of the y-axes to match the orientation. This can be set as such, ggconcurve(type = "consonance", data = df, position = "inverted")

    title

    A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333").

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, fill = "#333333").

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    Value

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    Plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels.

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    References

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    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

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    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

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    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

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    Examples

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    # Simulate random data - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - -(function1 <- ggconcurve(type = "consonance", intervalsdf))
    -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) - -RandomData2 <- data.frame(GroupA2, GroupB2) - -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2") - -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) - -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) - -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
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    All functions

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    curve_compare()

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    Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets

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    curve_corr()

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    Computes consonance intervals for correlations

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    curve_gen()

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    Computes consonance intervals for linear models

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    curve_lik()

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    Computes Likelihood Intervals for Ratio Data -

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    curve_mean()

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    Computes consonance intervals for mean differences

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    curve_meta()

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    Computes consonance intervals for meta-analysis data

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    curve_rev()

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    Reverse engineer consonance and surprisal functions from confidence limits and point estimates

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    curve_surv()

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    Produce Consonance Intervals for Survival Data

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    curve_table()

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    ggconcurve()

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    Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2

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    plot_compare()

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    Compares Different Functions on the Same Plot -

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    plot_concurve()

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    Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics.

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-<!-- %% ~~function to do ... ~~ --> — plot_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    plot_compare(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", xaxis = "Θ Range of Values", yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b")
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    data1

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    Examples

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    library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values")))
    -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) - -RandomData2 <- data.frame(GroupA2, GroupB2) - -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2") - -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
    - -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
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z{F&LCyeY~mpv=9)5|47-thB$w{_gX>6ZW59{rOw|U~%uZrvIZ+hFL}Zdw&ajR($}y R6ST$fxPOI0)VYRV{twHthhP8z diff --git a/docs/reference/plot_concurve.html b/docs/reference/plot_concurve.html deleted file mode 100644 index deab994..0000000 --- a/docs/reference/plot_concurve.html +++ /dev/null @@ -1,285 +0,0 @@ - - - - - - - - -Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics. — plot_concurve • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Takes the dataframe produced by the interval functions and plots the p-values, s-values, consonance (confidence) -levels, and the interval estimates to produce p- and s-value functions using base R graphics.

    -
    - -
    plot_concurve(type, data, measure, intervals, title, xlab, ylab1, ylab2, fontsize, fill)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example plot_concurve(type = "surprisal") or plot_concurve(type = "consonance").

    data

    Dataframe where the results from a curve_ function is stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    intervals

    Indicates whether the limits for different consonance levels should be plotted on the graph. By default, this is set to FALSE. When set to TRUE, it will plot 50%, 75%, 95%, and 99% intervals.

    title

    The title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, title = "Custom Title").

    xlab

    The label for the x-axis. By default, it is set to "Theta.". In order to set a label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, xlab = "Custom Caption").

    ylab1

    A label for the y-axis on the left side of the graph. By default, it is set to "P-value." In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab1= "Custom y-axis title").

    ylab2

    A label for the y-axis on the right side of the graph. By default, it is set to "Confidence Level."" In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab2= "Custom y-axis title").

    fontsize

    Controls the font size in the graphs. By default, it is set to size 12.).

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes.

    - -

    Value

    - -

    Plot with intervals at every consonance level graphed with their corresponding p- and s-values.

    -

    References

    - -

    Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: -There Is No Replication Crisis If We Don’t Expect Replication. Am Stat; 2018.

    -

    Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values -and their resolution with S-values. Am Stat. 2018;18(136).

    -

    Greenland S. The unconditional information in P-values, and its refutational interpretation -via S-values. 2018.

    -

    Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. -1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x

    -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    # Simulate random data - -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -RandomData <- data.frame(GroupA, GroupB) -RandomModel <- lm(GroupA ~ GroupB, data = RandomData) - -intervalsdf <- curve_gen(RandomModel, "GroupB") - -plot_concurve(type = "consonance", data = intervalsdf)
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index ce2552b..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,51 +0,0 @@ - - - - https://data.lesslikely.com/concurve//index.html - - - https://data.lesslikely.com/concurve//reference/curve_compare.html - - - https://data.lesslikely.com/concurve//reference/curve_corr.html - - - https://data.lesslikely.com/concurve//reference/curve_gen.html - - - https://data.lesslikely.com/concurve//reference/curve_lik.html - - - https://data.lesslikely.com/concurve//reference/curve_mean.html - - - https://data.lesslikely.com/concurve//reference/curve_meta.html - - - https://data.lesslikely.com/concurve//reference/curve_rev.html - - - https://data.lesslikely.com/concurve//reference/curve_surv.html - - - https://data.lesslikely.com/concurve//reference/curve_table.html - - - https://data.lesslikely.com/concurve//reference/defunct.html - - - https://data.lesslikely.com/concurve//reference/ggconcurve.html - - - https://data.lesslikely.com/concurve//reference/plot_compare.html - - - https://data.lesslikely.com/concurve//reference/plot_concurve.html - - - https://data.lesslikely.com/concurve//articles/examples.html - - - https://data.lesslikely.com/concurve//articles/stata.html - - From 855892aace3932559c52db8b34ce73e721e52814 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Sat, 30 Nov 2019 21:39:14 -0500 Subject: [PATCH 09/21] curve_boot --- .DS_Store | Bin 18436 -> 22532 bytes DESCRIPTION | 3 +- NAMESPACE | 1 + R/curve_boot.R | 183 +++++++++++++++++++++++++++++++--------------- R/curve_compare.R | 25 ++++--- R/curve_corr.R | 21 +++++- R/curve_gen.R | 42 ++++++----- R/curve_mean.R | 21 +++++- R/curve_meta.R | 20 ++++- R/curve_rev.R | 20 ++++- R/curve_surv.R | 20 ++++- R/curve_table.R | 1 + R/ggconcurve.R | 140 ++++++++++++++++++++++++----------- R/plot_compare.R | 30 ++++---- codemeta.json | 14 +++- 15 files changed, 384 insertions(+), 157 deletions(-) diff --git a/.DS_Store b/.DS_Store index a322411cd0f98c9ec4f4cc1f78a7af5957613b12..3955cf612e2f8a6a89e0662e1cdc65202fc15f2a 100644 GIT binary patch delta 3296 zcmeHJYitx%7`>;vZRc)FJ7vpscj@-Qc1ug4^ab>}ETvK^AP5D@L!fk*vQW0b_JJ5E zYSkbRsaHrqM1%1G7$Yu-Pl%#OBoIYZzz~IqB>cd4@P|>qJF`Vlj3)YnA2gZn&i!Vx z_nz;3=bY}1=za-S#u%#GeT9roqOS^FlX|R`(SG_8y{CS^38rwo`4?z(A43<|@a(u9bre=hwL;v$1bw(*!S#5_7nRRu~5Jf2OBam04@wg zDaufeN{qto7>Dt=15+>?cVQ0ZVF6mP6w468J?O>*co1vxC?3Z)Jb@?iBKG4|yoN(~ z7sqe{pED-Bm6?gLY$;pJnwgKavQ8Eh$4k;;d7N`h?bvGZWm&t3s%m!dROi${(C1$< zqcPMZ4+y&GPl*dBDVjN)9e=@u^aX;= zQ#%8`wuLhTK7X(+upr=H&Uuo_ULR~+;ytxJ~BK10U3y?V5ws7L0=A0OWsYMZ#! 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Chow Description: Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) , Schweder T, Hjort NL. (2002) , Singh K, Xie M, Strawderman WE. (2007) , Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) , Greenland S. (2019) , Chow ZR, Greenland S. (2019) , and Greenland S, Chow ZR. (2019) . Imports: parallel, - pbapply, + pbmcapply, compiler, boot, bcaboot, + ProfileLikelihood, ggplot2, metafor, dplyr, diff --git a/NAMESPACE b/NAMESPACE index 0a39cc9..d6ecf46 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,9 +1,10 @@ exportPattern("^[[:alpha:]]+") import(parallel) -import(pbapply) +import(pbmcapply) import(bcaboot) import(boot) import(ggplot2) +import(ProfileLikelihood) import(dplyr) import(tidyr) import(flextable) diff --git a/R/curve_boot.R b/R/curve_boot.R index a09bb82..79484b7 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -1,6 +1,6 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20000, steps = 1000, table = TRUE) { - pboptions(type = "timer", style = 1, char = "+") + # BCA Bootstrap Method --------------------------------------------------- @@ -19,15 +19,15 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 1:length(alpha) # Data Frame with BCA Intervals ------------------------------ - pboptions(type = "timer", style = 1, char = "+") - bca <- pblapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), cl = detectCores() - 1) + + bca <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), mc.cores = detectCores() - 1) bcaintervals <- data.frame(do.call(rbind, bca)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(bcaintervals) <- intrvl.limit - news <- pblapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), cl = detectCores() - 1) + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), mc.cores = detectCores() - 1) width <- data.frame(do.call(rbind, news)) colnames(width) <- "intrvl.width" - bews <- pblapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), cl = detectCores() - 1) + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = detectCores() - 1) levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" @@ -43,14 +43,14 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 # Data Frame with Standard Intervals ------------------------------ - std <- pblapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), cl = detectCores() - 1) + std <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), mc.cores = detectCores() - 1) stdintervals <- data.frame(do.call(rbind, std)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(stdintervals) <- intrvl.limit - news <- pblapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), cl = detectCores() - 1) + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), mc.cores = detectCores() - 1) width <- data.frame(do.call(rbind, news)) colnames(width) <- "intrvl.width" - bews <- pblapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), cl = detectCores() - 1) + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = detectCores() - 1) levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" @@ -91,9 +91,9 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 intrvls <- 1:steps / steps - t <- pblapply(intrvls, + t <- pbmclapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], - cl = detectCores() + mc.cores = detectCores() ) df <- data.frame(do.call(rbind, t)) diff --git a/R/curve_corr.R b/R/curve_corr.R index b9dc0d9..a0834a1 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -9,14 +9,14 @@ curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { stop("Error: 'steps' must be a numeric vector") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (0:steps) / steps - results <- pblapply(intrvls, FUN = function(i) { + results <- pbmclapply(intrvls, FUN = function(i) { cor.test(x, y, alternative = alternative, method = method, exact = NULL, conf.level = i, continuity = FALSE )$conf.int[] - }, cl = detectCores() - 1) + }, mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_gen.R b/R/curve_gen.R index 81eb2cb..0a6a815 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -10,13 +10,13 @@ curve_gen <- function(model, var, method = "wald", replicates = 1000, steps = 10 if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (0:steps) / steps if (method == "wald") { - results <- pblapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = detectCores() - 1) } else if (method == "glm") { require(MASS) - results <- pblapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], mc.cores = detectCores() - 1) } diff --git a/R/curve_lik.R b/R/curve_lik.R index 4263d3d..5bf1b50 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -11,14 +11,14 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (1:steps) / steps z <- qnorm(1 - intrvls / 2) if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- pblapply(z, FUN = function(i) point + (i * se), cl = detectCores() - 1) - UL <- pblapply(z, FUN = function(i) point - (i * se), cl = detectCores() - 1) + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -27,8 +27,8 @@ curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- pblapply(z, FUN = function(i) logpoint + (i * se), cl = detectCores() - 1) - logUL <- pblapply(z, FUN = function(i) logpoint - (i * se), cl = detectCores() - 1) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_mean.R b/R/curve_mean.R index 2611d5e..fd51b36 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -16,10 +16,10 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (0:steps) / steps if (method == "default") { - results <- pblapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], mc.cores = detectCores() - 1) } else if (method == "boot") { diff <- mean(x) - mean(y) if (paired) { @@ -33,7 +33,7 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = mean(sample(y, length(y), replace = T)) ) - diff } - results <- pblapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores() - 1) } df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") diff --git a/R/curve_meta.R b/R/curve_meta.R index 2218a48..a127bcd 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -10,9 +10,9 @@ curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (0:steps) / steps - results <- pblapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_rev.R b/R/curve_rev.R index 6ce90e4..ed01074 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -13,14 +13,14 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000, table = if (is.character(measure) != TRUE) { stop("Error: 'measure' must be a string such as 'default' or 'ratio'") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (1:steps) / steps z <- qnorm(1 - intrvls / 2) if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- pblapply(z, FUN = function(i) point + (i * se), cl = detectCores() - 1) - UL <- pblapply(z, FUN = function(i) point - (i * se), cl = detectCores() - 1) + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -29,8 +29,8 @@ curve_rev <- function(point, LL, UL, measure = "default", steps = 10000, table = else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- pblapply(z, FUN = function(i) logpoint + (i * se), cl = detectCores() - 1) - logUL <- pblapply(z, FUN = function(i) logpoint - (i * se), cl = detectCores() - 1) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_surv.R b/R/curve_surv.R index 96e6b8e..d4c61ae 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -7,9 +7,9 @@ curve_surv <- function(data, x, steps = 10000, table = TRUE) { if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - pboptions(type = "timer", style = 1, char = "+") + intrvls <- (1:steps) / steps - results <- pblapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], cl = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], mc.cores = detectCores() - 1) df <- data.frame(do.call(rbind, results))[, 3:4] intrvl.limit <- c("lower.limit", "upper.limit") diff --git a/R/curve_table.R b/R/curve_table.R index 932c698..845732f 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,7 +1,7 @@ curve_table <- function(data, levels, type = "data.frame") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - pboptions(type = "timer", style = 1, char = "+") - subdf <- pblapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), cl = detectCores() - 1) + + subdf <- pbmclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores() - 1) subdf <- data.frame(do.call(rbind, subdf)) class(subdf) <- c("data.frame", "concurve") subdf$intrvl.level <- (subdf$intrvl.level * 100) diff --git a/R/ggconcurve.R b/R/ggconcurve.R index fa6093d..1b4768d 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -42,7 +42,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- pblapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), cl = detectCores() - 1) + interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores() - 1) interval <- data.frame(do.call(rbind, interval)) interval <- pivot_longer(interval, X2:X3, names_to = "levels", values_to = "limits") interval <- interval[, -2] @@ -146,7 +146,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- pblapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), cl = detectCores() - 1) + interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores() - 1) interval <- data.frame(do.call(rbind, interval)) interval <- gather(interval, key = "levels", value = "limits", X2:X3) interval <- interval[, -2] @@ -223,7 +223,8 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul } ggplot(data = data, mapping = aes(x = x)) + - stat_ecdf(geom = "point", color = fill, size = 0.75, shape = 8) + + stat_ecdf(geom = "point", color = fill, size = 0.70, shape = 8, alpha = 0.3) + + geom_hline(yintercept = 0.50) + labs( title = "Consonance Distribution", subtitle = subtitle, @@ -232,7 +233,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), @@ -285,7 +286,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), @@ -345,7 +346,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), @@ -405,7 +406,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), @@ -465,7 +466,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), @@ -525,7 +526,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul ) + theme_bw() + theme( - plot.title = element_text(size = 16), + plot.title = element_text(size = 14), plot.subtitle = element_text(size = 12), plot.caption = element_text(size = 8), axis.title.x = element_text(size = 13), diff --git a/codemeta.json b/codemeta.json index 45131d9..7532ef4 100644 --- a/codemeta.json +++ b/codemeta.json @@ -88,15 +88,15 @@ }, { "@type": "SoftwareApplication", - "identifier": "pbapply", - "name": "pbapply", + "identifier": "pbmcapply", + "name": "pbmcapply", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=pbapply" + "sameAs": "https://CRAN.R-project.org/package=pbmcapply" }, { "@type": "SoftwareApplication", @@ -127,6 +127,18 @@ }, "sameAs": "https://CRAN.R-project.org/package=bcaboot" }, + { + "@type": "SoftwareApplication", + "identifier": "ProfileLikelihood", + "name": "ProfileLikelihood", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=ProfileLikelihood" + }, { "@type": "SoftwareApplication", "identifier": "ggplot2", @@ -261,7 +273,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - 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If you've never heard of a [reprex](https://reprex.tidyverse.org/) before, start by reading . + +--- + +Brief description of the problem + +```r +# insert reprex here +``` diff --git a/.github/SUPPORT.md b/.github/SUPPORT.md new file mode 100644 index 0000000..1c02b30 --- /dev/null +++ b/.github/SUPPORT.md @@ -0,0 +1,35 @@ +# Getting help with concurve + +Thanks for using concurve. Before filing an issue, there are a few places +to explore and pieces to put together to make the process as smooth as possible. + +Start by making a minimal **repr**oducible **ex**ample using the +[reprex](https://reprex.tidyverse.org/) package. If you haven't heard of or used +reprex before, you're in for a treat! Seriously, reprex will make all of your +R-question-asking endeavors easier (which is a pretty insane ROI for the five to +ten minutes it'll take you to learn what it's all about). For additional reprex +pointers, check out the [Get help!](https://www.tidyverse.org/help/) section of +the tidyverse site. + +Armed with your reprex, the next step is to figure out [where to ask](https://www.tidyverse.org/help/#where-to-ask). + + * If it's a question: start with [community.rstudio.com](https://community.rstudio.com/), + and/or StackOverflow. There are more people there to answer questions. + * If it's a bug: you're in the right place, file an issue. + * If you're not sure: let the community help you figure it out! If your + problem _is_ a bug or a feature request, you can easily return here and + report it. + +Before opening a new issue, be sure to [search issues and pull requests](https://github.com/tidyverse/concurve/issues) to make sure the +bug hasn't been reported and/or already fixed in the development version. By +default, the search will be pre-populated with `is:issue is:open`. You can +[edit the qualifiers](https://help.github.com/articles/searching-issues-and-pull-requests/) +(e.g. `is:pr`, `is:closed`) as needed. For example, you'd simply +remove `is:open` to search _all_ issues in the repo, open or closed. + + +If you _are_ in the right place, and need to file an issue, please review the +["File issues"](https://www.tidyverse.org/contribute/#issues) paragraph from +the tidyverse contributing guidelines. + +Thanks for your help! diff --git a/.travis.yml b/.travis.yml index 3a41e43..5ac18d0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,12 +1,15 @@ # R for travis: see documentation at https://docs.travis-ci.com/user/languages/r language: R -sudo: false cache: packages -r_packages: - - covr - -after_success: - - Rscript -e 'library(covr); codecov()' - - Rscript -e 'covr::coveralls()' +matrix: + include: + - r: devel + - r: release + after_success: + - Rscript -e 'covr::codecov()' + - r: oldrel + - r: 3.4 + - r: 3.3 + - r: 3.2 diff --git a/DESCRIPTION b/DESCRIPTION index c0b9479..48a8041 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,44 +1,73 @@ -Package: concurve Type: Package -Date: 2019-12-01 -Title: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions +Package: concurve +Title: Computes and Plots Compatibility (Confidence) Intervals, + P-Values, S-Values, & Likelihood Intervals to Form Consonance, + Surprisal, & Likelihood Functions Version: 2.2.0 -Authors@R: c( - person("Zad R.", "Chow", , "zad@lesslikely.com", role = c("aut", "cre"), - comment = c(ORCID = "0000-0003-1545-8199") - ), - person("Andrew D.", "Vigotsky", role = "aut", - comment = c(ORCID = "0000-0003-3166-0688") - ) - ) +Date: 2019-12-01 +Authors@R: + c(person(given = "Zad R.", + family = "Chow", + role = c("aut", "cre"), + email = "zad@lesslikely.com", + comment = c(ORCID = "0000-0003-1545-8199")), + person(given = "Andrew D.", + family = "Vigotsky", + role = "aut", + comment = c(ORCID = "0000-0003-3166-0688"))) Maintainer: Zad R. Chow -Description: Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) , Schweder T, Hjort NL. (2002) , Singh K, Xie M, Strawderman WE. (2007) , Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) , Greenland S. (2019) , Chow ZR, Greenland S. (2019) , and Greenland S, Chow ZR. (2019) . -Imports: parallel, - pbmcapply, - compiler, - boot, - bcaboot, - ProfileLikelihood, - ggplot2, - metafor, - dplyr, - tidyr, - flextable, - officer, - knitr, - tibble, - survival, - survminer, - scales -Suggests: - testthat, - covr -License: GPL-3 | file LICENSE -URL: https://data.lesslikely.com/concurve/, https://github.com/Zadchow/concurve, https://lesslikely.com/ +Description: Allows one to compute consonance (confidence) + intervals for various statistical tests along with their corresponding + P-values, S-values, and likelihoods. The intervals can be plotted to + create consonance, surprisal, and likelihood functions allowing one to + see what effect sizes are compatible with the test model at various + consonance levels rather than being limited to one interval estimate + such as 95%. These methods are discussed by Poole C. (1987) + , Schweder T, Hjort NL. (2002) + , Singh K, Xie M, Strawderman WE. (2007) + , Rothman KJ, Greenland S, Lash TL. (2008, + ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) + , Greenland S. (2019) + , Chow ZR, Greenland S. (2019) + , and Greenland S, Chow ZR. (2019) + . +License: GPL-3 +URL: https://data.lesslikely.com/concurve/, + https://github.com/Zadchow/concurve, https://lesslikely.com/ BugReports: https://github.com/Zadchow/concurve/issues -VignetteBuilder: knitr -Encoding: UTF-8 +Imports: + bcaboot, + boot, + compiler, + dplyr, + flextable, + ggplot2, + knitr, + metafor, + officer, + parallel, + pbmcapply, + ProfileLikelihood, + rlang (>= 0.1.2), + scales, + survival, + survminer, + tibble, + tidyr, + magrittr +Suggests: + covr, + roxygen2, + spelling, + testthat +VignetteBuilder: + knitr ByteCompile: true +Encoding: UTF-8 +Language: en-US LazyData: true RoxygenNote: 7.0.1 -X-schema.org-keywords: confidence, compatibility, consonance, surprisal, interval, function, curve +X-schema.org-keywords: confidence, compatibility, consonance, + surprisal, interval, function, curve +Depends: + R (>= 3.2) diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..0010197 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,595 @@ +GNU General Public License +========================== + +_Version 3, 29 June 2007_ +_Copyright © 2007 Free Software Foundation, Inc. <>_ + +Everyone is permitted to copy and distribute verbatim copies of this license +document, but changing it is not allowed. + +## Preamble + +The GNU General Public License is a free, copyleft license for software and other +kinds of works. + +The licenses for most software and other practical works are designed to take away +your freedom to share and change the works. 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Additional Terms + +“Additional permissions” are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. Additional +permissions that are applicable to the entire Program shall be treated as though they +were included in this License, to the extent that they are valid under applicable +law. If additional permissions apply only to part of the Program, that part may be +used separately under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + +When you convey a copy of a covered work, you may at your option remove any +additional permissions from that copy, or from any part of it. (Additional +permissions may be written to require their own removal in certain cases when you +modify the work.) 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You may not convey a covered work if you are a party to an arrangement with +a third party that is in the business of distributing software, under which you make +payment to the third party based on the extent of your activity of conveying the +work, and under which the third party grants, to any of the parties who would receive +the covered work from you, a discriminatory patent license **(a)** in connection with +copies of the covered work conveyed by you (or copies made from those copies), or **(b)** +primarily for and in connection with specific products or compilations that contain +the covered work, unless you entered into that arrangement, or that patent license +was granted, prior to 28 March 2007. + +Nothing in this License shall be construed as excluding or limiting any implied +license or other defenses to infringement that may otherwise be available to you +under applicable patent law. + +### 12. No Surrender of Others' Freedom + +If conditions are imposed on you (whether by court order, agreement or otherwise) +that contradict the conditions of this License, they do not excuse you from the +conditions of this License. If you cannot convey a covered work so as to satisfy +simultaneously your obligations under this License and any other pertinent +obligations, then as a consequence you may not convey it at all. For example, if you +agree to terms that obligate you to collect a royalty for further conveying from +those to whom you convey the Program, the only way you could satisfy both those terms +and this License would be to refrain entirely from conveying the Program. + +### 13. Use with the GNU Affero General Public License + +Notwithstanding any other provision of this License, you have permission to link or +combine any covered work with a work licensed under version 3 of the GNU Affero +General Public License into a single combined work, and to convey the resulting work. +The terms of this License will continue to apply to the part which is the covered +work, but the special requirements of the GNU Affero General Public License, section +13, concerning interaction through a network will apply to the combination as such. + +### 14. Revised Versions of this License + +The Free Software Foundation may publish revised and/or new versions of the GNU +General Public License from time to time. Such new versions will be similar in spirit +to the present version, but may differ in detail to address new problems or concerns. + +Each version is given a distinguishing version number. If the Program specifies that +a certain numbered version of the GNU General Public License “or any later +version” applies to it, you have the option of following the terms and +conditions either of that numbered version or of any later version published by the +Free Software Foundation. If the Program does not specify a version number of the GNU +General Public License, you may choose any version ever published by the Free +Software Foundation. + +If the Program specifies that a proxy can decide which future versions of the GNU +General Public License can be used, that proxy's public statement of acceptance of a +version permanently authorizes you to choose that version for the Program. + +Later license versions may give you additional or different permissions. However, no +additional obligations are imposed on any author or copyright holder as a result of +your choosing to follow a later version. + +### 15. Disclaimer of Warranty + +THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. +EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER +EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE +QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE +DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +### 16. Limitation of Liability + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY +COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS +PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, +INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE +PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE +OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE +WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +### 17. Interpretation of Sections 15 and 16 + +If the disclaimer of warranty and limitation of liability provided above cannot be +given local legal effect according to their terms, reviewing courts shall apply local +law that most closely approximates an absolute waiver of all civil liability in +connection with the Program, unless a warranty or assumption of liability accompanies +a copy of the Program in return for a fee. + +_END OF TERMS AND CONDITIONS_ + +## How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest possible use to +the public, the best way to achieve this is to make it free software which everyone +can redistribute and change under these terms. + +To do so, attach the following notices to the program. It is safest to attach them +to the start of each source file to most effectively state the exclusion of warranty; +and each file should have at least the “copyright” line and a pointer to +where the full notice is found. + + + Copyright (C) 2019 NULL + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + concurve Copyright (C) 2019 NULL + This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type 'show c' for details. + +The hypothetical commands `show w` and `show c` should show the appropriate parts of +the General Public License. Of course, your program's commands might be different; +for a GUI interface, you would use an “about box”. + +You should also get your employer (if you work as a programmer) or school, if any, to +sign a “copyright disclaimer” for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +<>. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider it +more useful to permit linking proprietary applications with the library. If this is +what you want to do, use the GNU Lesser General Public License instead of this +License. But first, please read +<>. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..d22ee17 --- /dev/null +++ b/Makefile @@ -0,0 +1,26 @@ +# h/t to @jimhester and @yihui for this parse block: +# https://github.com/yihui/knitr/blob/dc5ead7bcfc0ebd2789fe99c527c7d91afb3de4a/Makefile#L1-L4 +# Note the portability change as suggested in the manual: +# https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Writing-portable-packages +PKGNAME = `sed -n "s/Package: *\([^ ]*\)/\1/p" DESCRIPTION` +PKGVERS = `sed -n "s/Version: *\([^ ]*\)/\1/p" DESCRIPTION` + + +all: check + +build: + R CMD build . + +check: build + R CMD check --no-manual $(PKGNAME)_$(PKGVERS).tar.gz + +install_deps: + Rscript \ + -e 'if (!requireNamespace("remotes") install.packages("remotes")' \ + -e 'remotes::install_deps(dependencies = TRUE)' + +install: install_deps build + R CMD INSTALL $(PKGNAME)_$(PKGVERS).tar.gz + +clean: + @rm -rf $(PKGNAME)_$(PKGVERS).tar.gz $(PKGNAME).Rcheck diff --git a/R/curve_boot.R b/R/curve_boot.R index 79484b7..b5ebb28 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -3,7 +3,7 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 -# BCA Bootstrap Method --------------------------------------------------- + # BCA Bootstrap Method --------------------------------------------------- if (method == "bca") { intrvls <- 0.5 / steps @@ -69,9 +69,9 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (bca_subintervals <- (curve_table(df_bca, levels, type = "data.frame"))) + (bca_subintervals <- (curve_table(df_bca, levels, type = "c", format = "data.frame"))) class(bca_subintervals) <- c("data.frame", "concurve") - (std_subintervals <- (curve_table(df_std, levels, type = "data.frame"))) + (std_subintervals <- (curve_table(df_std, levels, type = "c", format = "data.frame"))) class(std_subintervals) <- c("data.frame", "concurve") dataframes <- list(df_std, std_subintervals, df_bca, bca_subintervals) names(dataframes) <- c("Standard Intervals", "Standard Table", "BCA Intervals", "BCA Table") @@ -116,7 +116,7 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_corr.R b/R/curve_corr.R index a0834a1..6e1bd78 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -35,7 +35,7 @@ curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_gen.R b/R/curve_gen.R index 0a6a815..93f8fac 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -38,7 +38,7 @@ curve_gen <- function(model, var, method = "wald", replicates = 1000, steps = 10 if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_lik.R b/R/curve_lik.R index 5bf1b50..f6f55d7 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -1,60 +1,25 @@ -curve_lik <- function(point, LL, UL, measure = "ratio", steps = 10000) { - if (is.numeric(point) != TRUE) { - stop("Error: 'x' must be a numeric vector") +curve_lik <- function(likobject, data, table = TRUE) { + values <- likobject[[1]] # theta values + likelihood <- likobject[[2]] # profile likelihoods + support <- likobject[[3]] # normalized profile likelihoods + loglikelihood <- log(support) # log of normalized profile likelihoods + deviancestat <- -(loglikelihood) # deviance statistic + + likfunction <- data.frame(values, likelihood, loglikelihood, support, deviancestat) + class(likfunction) <- c("data.frame", "concurve") + + + if (table == TRUE) { + levels <- c(0.03, 0.05, 0.12, 0.14) + (df_subintervals <- (curve_table(likfunction, levels, type = "l", format = "data.frame"))) + class(df_subintervals) <- c("data.frame", "concurve") + dataframes <- list(likfunction, df_subintervals) + names(dataframes) <- c("Intervals Dataframe", "Intervals Table") + class(dataframes) <- "concurve" + return(dataframes) + } else if (table == FALSE) { + return(list(likfunction)) } - if (is.numeric(LL) != TRUE) { - stop("Error: 'y' must be a numeric vector") - } - if (is.numeric(UL) != TRUE) { - stop("Error: 'y' must be a numeric vector") - } - if (is.character(measure) != TRUE) { - stop("Error: 'measure' must be a string such as 'default' or 'ratio'") - } - - intrvls <- (1:steps) / steps - z <- qnorm(1 - intrvls / 2) - - if (measure == "default") { - se <- (UL / LL) / 3.92 - LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) - UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) - df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) - intrvl.limit <- c("lower.limit", "upper.limit") - colnames(df) <- intrvl.limit - } - - else if (measure == "ratio") { - se <- log(UL / LL) / 3.92 - logpoint <- log(point) - logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) - logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) - df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) - intrvl.limit <- c("lower.limit", "upper.limit") - colnames(df) <- intrvl.limit - df$lower.limit <- exp(df$lower.limit) - df$upper.limit <- exp(df$upper.limit) - } - - df$intrvl.level <- 1 - intrvls - df$pvalue <- 1 - (1 - intrvls) - df$svalue <- -log2(df$pvalue) - df <- head(df, -1) - - - se <- log(UL / LL) / 3.92 - values <- seq(from = df[1, 1], to = df[1, 2], by = 0.01) - zscore <- sapply( - values, - function(j) (log(j / point) / se) - ) - - support <- exp((-zscore^2) / 2) - deviancestat <- (zscore^2) - likelihood <- support * (log(point)) - loglikelihood <- log(likelihood) - likfunction <- data.frame(values, zscore, likelihood, loglikelihood, support, deviancestat) - return(likfunction) } -utils::globalVariables(c("likfunction", "values", "zscore", "likelihood", "loglikelihood", "support", "deviancestat")) +utils::globalVariables(c("likfunction", "values", "likelihood", "loglikelihood", "support", "deviancestat")) diff --git a/R/curve_mean.R b/R/curve_mean.R index fd51b36..a00fd1b 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -53,7 +53,7 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_meta.R b/R/curve_meta.R index a127bcd..9bec2cb 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -37,7 +37,7 @@ curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_rev.R b/R/curve_rev.R index ed01074..3221740 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -1,66 +1,132 @@ -# Reverse Engineer Consonance Functions Using the Point Estimate and Confidence Limits +# Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits -curve_rev <- function(point, LL, UL, measure = "default", steps = 10000, table = TRUE) { - if (is.numeric(point) != TRUE) { - stop("Error: 'x' must be a numeric vector") - } - if (is.numeric(LL) != TRUE) { - stop("Error: 'y' must be a numeric vector") - } - if (is.numeric(UL) != TRUE) { - stop("Error: 'y' must be a numeric vector") - } - if (is.character(measure) != TRUE) { - stop("Error: 'measure' must be a string such as 'default' or 'ratio'") - } +curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10000, table = TRUE) { - intrvls <- (1:steps) / steps - z <- qnorm(1 - intrvls / 2) - if (measure == "default") { - se <- (UL / LL) / 3.92 - LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) - UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) - df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) - intrvl.limit <- c("lower.limit", "upper.limit") - colnames(df) <- intrvl.limit - } + # Produce Consonance / Surprisal Functions -------------------------------- + + if (type == "c") { + if (is.numeric(point) != TRUE) { + stop("Error: 'x' must be a numeric vector") + } + if (is.numeric(LL) != TRUE) { + stop("Error: 'y' must be a numeric vector") + } + if (is.numeric(UL) != TRUE) { + stop("Error: 'y' must be a numeric vector") + } + if (is.character(measure) != TRUE) { + stop("Error: 'measure' must be a string such as 'default' or 'ratio'") + } + + intrvls <- (1:steps) / steps + z <- qnorm(1 - intrvls / 2) + + if (measure == "default") { + se <- (UL / LL) / 3.92 + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) + df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(df) <- intrvl.limit + } + + else if (measure == "ratio") { + se <- log(UL / LL) / 3.92 + logpoint <- log(point) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) + df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(df) <- intrvl.limit + df$lower.limit <- exp(df$lower.limit) + df$upper.limit <- exp(df$upper.limit) + } + df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) + df$intrvl.level <- 1 - intrvls + df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 + df$pvalue <- 1 - (1 - intrvls) + df$svalue <- -log2(df$pvalue) + df <- head(df, -1) + class(df) <- c("data.frame", "concurve") + densdf <- data.frame(c(df$lower.limit, df$upper.limit)) + colnames(densdf) <- "x" + densdf <- head(densdf, -1) + class(densdf) <- c("data.frame", "concurve") + + if (table == TRUE) { + levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) + class(df_subintervals) <- c("data.frame", "concurve") + dataframes <- list(df, densdf, df_subintervals) + names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") + class(dataframes) <- "concurve" + return(dataframes) + } else if (table == FALSE) { + return(list(df, densdf)) + } + + + # Produce Likelihood / Deviance Functions --------------------------------- + } else if (type == "l") { + intrvls <- (1:steps) / steps + z <- qnorm(1 - intrvls / 2) + + if (measure == "default") { + se <- (UL / LL) / 3.92 + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) + df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(df) <- intrvl.limit + } + + else if (measure == "ratio") { + se <- log(UL / LL) / 3.92 + logpoint <- log(point) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) + df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) + intrvl.limit <- c("lower.limit", "upper.limit") + colnames(df) <- intrvl.limit + df$lower.limit <- exp(df$lower.limit) + df$upper.limit <- exp(df$upper.limit) + } + + df$intrvl.level <- 1 - intrvls + df$pvalue <- 1 - (1 - intrvls) + df$svalue <- -log2(df$pvalue) + df <- head(df, -1) + - else if (measure == "ratio") { se <- log(UL / LL) / 3.92 - logpoint <- log(point) - logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) - logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) - df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) - intrvl.limit <- c("lower.limit", "upper.limit") - colnames(df) <- intrvl.limit - df$lower.limit <- exp(df$lower.limit) - df$upper.limit <- exp(df$upper.limit) - } - df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) - df$intrvl.level <- 1 - intrvls - df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 - df$pvalue <- 1 - (1 - intrvls) - df$svalue <- -log2(df$pvalue) - df <- head(df, -1) - class(df) <- c("data.frame", "concurve") - densdf <- data.frame(c(df$lower.limit, df$upper.limit)) - colnames(densdf) <- "x" - densdf <- head(densdf, -1) - class(densdf) <- c("data.frame", "concurve") - - if (table == TRUE) { - levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) - class(df_subintervals) <- c("data.frame", "concurve") - dataframes <- list(df, densdf, df_subintervals) - names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") - class(dataframes) <- "concurve" - return(dataframes) - } else if (table == FALSE) { - return(list(df, densdf)) + values <- seq(from = df[1, 1], to = df[1, 2], by = 0.01) + zscore <- sapply( + values, + function(j) (log(j / point) / se) + ) + + support <- exp((-zscore^2) / 2) + deviancestat <- (zscore^2) + likelihood <- support * (log(point)) + loglikelihood <- log(likelihood) + likfunction <- data.frame(values, likelihood, loglikelihood, support, deviancestat) + + + if (table == TRUE) { + levels <- c(0.03, 0.05, 0.12, 0.14) + (df_subintervals <- (curve_table(likfunction, levels, type = "l", format = "data.frame"))) + class(df_subintervals) <- c("data.frame", "concurve") + dataframes <- list(likfunction, df_subintervals) + names(dataframes) <- c("Intervals Dataframe", "Intervals Table") + class(dataframes) <- "concurve" + return(dataframes) + } else if (table == FALSE) { + return(list(likfunction)) + } } } # RMD Check utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) +utils::globalVariables(c("likfunction", "values", "likelihood", "loglikelihood", "support", "deviancestat")) diff --git a/R/curve_surv.R b/R/curve_surv.R index d4c61ae..42490d5 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -28,7 +28,7 @@ curve_surv <- function(data, x, steps = 10000, table = TRUE) { if (table == TRUE) { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + (df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame"))) class(df_subintervals) <- c("data.frame", "concurve") dataframes <- list(df, densdf, df_subintervals) names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") diff --git a/R/curve_table.R b/R/curve_table.R index 845732f..9386775 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,37 +1,48 @@ -curve_table <- function(data, levels, type = "data.frame") { - levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) +curve_table <- function(data, levels, type = "c", format = "data.frame") { + if (type == "c") { + levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - subdf <- pbmclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores() - 1) - subdf <- data.frame(do.call(rbind, subdf)) - class(subdf) <- c("data.frame", "concurve") - subdf$intrvl.level <- (subdf$intrvl.level * 100) - subcolnames <- c("Lower Limit", "Upper Limit", "Interval Width", "Interval Level (%)", "CDF", "P-value", "S-value (bits)") - colnames(subdf) <- subcolnames - subdf <- round(subdf, digits = 3) + subdf <- pbmclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores() - 1) + subdf <- data.frame(do.call(rbind, subdf)) + class(subdf) <- c("data.frame", "concurve") + subdf$intrvl.level <- (subdf$intrvl.level * 100) + subcolnames <- c("Lower Limit", "Upper Limit", "Interval Width", "Interval Level (%)", "CDF", "P-value", "S-value (bits)") + colnames(subdf) <- subcolnames + subdf <- round(subdf, digits = 3) + } else if (type == "l") { + levels <- c(0.03, 0.05, 0.12, 0.14) - if (type == "data.frame") { + subdf <- pbmclapply(levels, FUN = function(i) (subset(data, round(support, 2) == i)), mc.cores = detectCores() - 1) + subdf <- data.frame(do.call(rbind, subdf)) + class(subdf) <- c("data.frame", "concurve") + subcolnames <- c("Theta", "Likelihood", "Log Likelihood", "Relative Likelihood", "Deviance Statistic") + colnames(subdf) <- subcolnames + subdf <- round(subdf, digits = 3) + } + + if (format == "data.frame") { return(subdf) - } else if (type == "tibble") { + } else if (format == "tibble") { subdf <- tibble::tibble(subdf) return(subdf) - } else if (type == "docx") { + } else if (format == "docx") { subdf <- flextable(subdf) subdf <- autofit(subdf) subdf return(print(subdf, preview = "docx")) - } else if (type == "pptx") { + } else if (format == "pptx") { subdf <- flextable(subdf) subdf <- autofit(subdf) subdf return(print(subdf, preview = "pptx")) - } else if (type == "latex") { + } else if (format == "latex") { subdf <- knitr::kable( subdf, booktabs = TRUE, label = "A table of some interval estimates at various levels and corresponding statistics." ) return(subdf) - } else if (type == "image") { + } else if (format == "image") { subdf <- flextable(subdf) subdf <- autofit(subdf) subdf diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 1b4768d..8079c4d 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -313,7 +313,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Relative Likelihood Function ----------------------------------------------------- } else if (type == "l1") { - if (ncol(data) != 6) { + if (ncol(data) != 5) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -342,7 +342,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul title = "Relative Likelihood Function", subtitle = subtitle, x = xaxis, - y = "Relative Likelihood \n(1/MLR)" + y = "Relative Likelihood" ) + theme_bw() + theme( @@ -371,9 +371,9 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul } } - # Log-Likelihood Function ----------------------------------------------------- + # Log Likelihood Function ----------------------------------------------------- } else if (type == "l2") { - if (ncol(data) != 6) { + if (ncol(data) != 5) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -399,7 +399,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul geom_line() + geom_ribbon(aes(x = values, ymin = min(loglikelihood), ymax = loglikelihood), fill = "#239a98", alpha = 0.30) + labs( - title = "Log-Likelihood Function", + title = "Log Likelihood Function", subtitle = subtitle, x = xaxis, y = "Log Likelihood" @@ -433,7 +433,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Likelihood Function ----------------------------------------------------- } else if (type == "l3") { - if (ncol(data) != 6) { + if (ncol(data) != 5) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -493,7 +493,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Deviance Function ----------------------------------------------------- } else if (type == "d") { - if (ncol(data) != 6) { + if (ncol(data) != 5) { stop("Error: 'data' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -522,7 +522,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul title = "Deviance Function", subtitle = subtitle, x = xaxis, - y = "Deviance Statistic \n2ln(MLR)" + y = "Deviance Statistic" ) + theme_bw() + theme( @@ -536,20 +536,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + - scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0.0075, 0.0075)) + - if (nullvalue == TRUE) { - if (measure == "default") { - annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .8, linetype = 1 - ) - } else if (measure == "ratio") { - annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .8, linetype = 1 - ) - } - } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0.0075, 0.0075)) } } diff --git a/R/plot_compare.R b/R/plot_compare.R index ae67ced..88d6d28 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -257,10 +257,10 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu # Relative Likelihood Function ----------------------------------------------------- } else if (type == "l1") { - if (ncol(data1) != 6) { + if (ncol(data1) != 5) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 5) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -294,7 +294,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu title = "Relative Likelihood Functions", subtitle = subtitle, x = xaxis, - y = "Relative Likelihood \n(1/MLR)" + y = "Relative Likelihood" ) + theme_bw() + theme( @@ -345,10 +345,10 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu # Log-Likelihood Function ----------------------------------------------------- } else if (type == "l2") { - if (ncol(data1) != 6) { + if (ncol(data1) != 5) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 5) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -379,10 +379,10 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu geom_line(data = data2) + geom_ribbon(data = data2, aes(x = values, ymin = min(loglikelihood), ymax = loglikelihood, fill = fill2), alpha = 0.30) + labs( - title = "Log-Likelihood Functions", + title = "Log Likelihood Function", subtitle = subtitle, x = xaxis, - y = "Log-Likelihood" + y = "Log Likelihood" ) + theme_bw() + theme( @@ -433,10 +433,10 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu # Likelihood Function ----------------------------------------------------- } else if (type == "l3") { - if (ncol(data1) != 6) { + if (ncol(data1) != 5) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 5) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -467,7 +467,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu geom_line(data = data2) + geom_ribbon(data = data2, aes(x = values, ymin = min(likelihood), ymax = likelihood, fill = fill2), alpha = 0.30) + labs( - title = "Likelihood Functions", + title = "Likelihood Function", subtitle = subtitle, x = xaxis, y = "Likelihood" @@ -520,10 +520,10 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu # Deviance Function ----------------------------------------------------- } else if (type == "d") { - if (ncol(data1) != 6) { + if (ncol(data1) != 5) { stop("Error: 'data1' must be a data frame from 'concurve'.") } - if (ncol(data2) != 6) { + if (ncol(data2) != 5) { stop("Error: 'data2' must be a data frame from 'concurve'.") } if (is.character(measure) != TRUE) { @@ -590,20 +590,7 @@ plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalu { if (measure == "ratio") scale_x_log10(breaks = scales::pretty_breaks(n = 10)) } + - scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0.0075, 0.0075)) + - if (nullvalue == TRUE) { - if (measure == "default") { - annotate("segment", - x = 0, xend = 0, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .8, linetype = 1 - ) - } else if (measure == "ratio") { - annotate("segment", - x = 1, xend = 1, y = 0, yend = 1, - color = "#990000", alpha = 0.3, size = .8, linetype = 1 - ) - } - } + scale_y_continuous(breaks = scales::pretty_breaks(n = 10), expand = c(0.0075, 0.0075)) } } diff --git a/R/utils-pipe.R b/R/utils-pipe.R new file mode 100644 index 0000000..e79f3d8 --- /dev/null +++ b/R/utils-pipe.R @@ -0,0 +1,11 @@ +#' Pipe operator +#' +#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +#' +#' @name %>% +#' @rdname pipe +#' @keywords internal +#' @export +#' @importFrom magrittr %>% +#' @usage lhs \%>\% rhs +NULL diff --git a/R/utils-tidy-eval.R b/R/utils-tidy-eval.R new file mode 100644 index 0000000..af7c474 --- /dev/null +++ b/R/utils-tidy-eval.R @@ -0,0 +1,47 @@ +#' Tidy eval helpers +#' +#' @description +#' +#' * \code{\link[rlang:quotation]{sym}()} creates a symbol from a string and +#' \code{\link[rlang:quotation]{syms}()} creates a list of symbols from a +#' character vector. +#' +#' * \code{\link[rlang:quotation]{enquo}()} and +#' \code{\link[rlang:quotation]{enquos}()} delay the execution of one or +#' several function arguments. \code{enquo()} returns a single quoted +#' expression, which is like a blueprint for the delayed computation. +#' \code{enquos()} returns a list of such quoted expressions. +#' +#' * \code{\link[rlang:quotation]{expr}()} quotes a new expression _locally_. It +#' is mostly useful to build new expressions around arguments +#' captured with [enquo()] or [enquos()]: +#' \code{expr(mean(!!enquo(arg), na.rm = TRUE))}. +#' +#' * \code{\link[rlang]{as_name}()} transforms a quoted variable name +#' into a string. Supplying something else than a quoted variable +#' name is an error. +#' +#' That's unlike \code{\link[rlang]{as_label}()} which also returns +#' a single string but supports any kind of R object as input, +#' including quoted function calls and vectors. Its purpose is to +#' summarise that object into a single label. That label is often +#' suitable as a default name. +#' +#' If you don't know what a quoted expression contains (for instance +#' expressions captured with \code{enquo()} could be a variable +#' name, a call to a function, or an unquoted constant), then use +#' \code{as_label()}. If you know you have quoted a simple variable +#' name, or would like to enforce this, use \code{as_name()}. +#' +#' To learn more about tidy eval and how to use these tools, visit +#' \url{https://tidyeval.tidyverse.org} and the +#' \href{https://adv-r.hadley.nz/metaprogramming.html}{Metaprogramming +#' section} of \href{https://adv-r.hadley.nz}{Advanced R}. +#' +#' @md +#' @name tidyeval +#' @keywords internal +#' @importFrom rlang expr enquo enquos sym syms .data := as_name as_label +#' @aliases expr enquo enquos sym syms .data := as_name as_label +#' @export expr enquo enquos sym syms .data := as_name as_label +NULL diff --git a/README.Rmd b/README.Rmd index 93b940d..3c815ae 100644 --- a/README.Rmd +++ b/README.Rmd @@ -4,15 +4,17 @@ output: github_document --- concurve | Graph Interval Functions ================ - -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/concurve)](https://cran.r-project.org/package=concurve) -[![Build Status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) -[![Build status](https://ci.appveyor.com/api/projects/status/v8sp9x96dap2om9s?svg=true)](https://ci.appveyor.com/project/Zadchow/concurve) -[![DOI](https://zenodo.org/badge/165464881.svg)](https://zenodo.org/badge/latestdoi/165464881) + +[![CRAN status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) +[![Travis build status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) +[![Codecov test coverage](https://codecov.io/gh/Zadchow/concurve/branch/master/graph/badge.svg)](https://codecov.io/gh/Zadchow/concurve?branch=master) +[![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran.r-project.org/package=concurve) [![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) + + * * * # Examples diff --git a/README.md b/README.md index 2d9469e..bfa3e83 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,22 @@ # concurve | Graph Interval Functions + + [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/concurve)](https://cran.r-project.org/package=concurve) [![Build Status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) [![Build status](https://ci.appveyor.com/api/projects/status/v8sp9x96dap2om9s?svg=true)](https://ci.appveyor.com/project/Zadchow/concurve) -[![DOI](https://zenodo.org/badge/165464881.svg)](https://zenodo.org/badge/latestdoi/165464881) +[![Lifecycle: +maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran.r-project.org/package=concurve) [![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) +[![CRAN +status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) + ----- @@ -204,10 +210,8 @@ install_github("zadchow/concurve") ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: - ## [1] stats graphics grDevices utils datasets methods base + ## [1] stats graphics grDevices utils methods base ## ## loaded via a namespace (and not attached): - ## [1] compiler_3.6.1 magrittr_1.5 tools_3.6.1 htmltools_0.4.0 - ## [5] yaml_2.2.0 Rcpp_1.0.3 stringi_1.4.3 rmarkdown_1.17 - ## [9] knitr_1.26 stringr_1.4.0 xfun_0.11 digest_0.6.23 - ## [13] rlang_0.4.2 evaluate_0.14 + ## [1] compiler_3.6.1 magrittr_1.5 tools_3.6.1 htmltools_0.4.0 yaml_2.2.0 Rcpp_1.0.3 stringi_1.4.3 + ## [8] rmarkdown_1.18 knitr_1.26 stringr_1.4.0 xfun_0.11 digest_0.6.23 rlang_0.4.2 evaluate_0.14 diff --git a/codemeta.json b/codemeta.json index 7532ef4..5476838 100644 --- a/codemeta.json +++ b/codemeta.json @@ -5,8 +5,8 @@ ], "@type": "SoftwareSourceCode", "identifier": "concurve", - "description": "Allows one to compute consonance (confidence) intervals for various statistical tests along with their corresponding P-values, S-values, and likelihoods. The intervals can be plotted to create consonance, surprisal, and likelihood functions allowing one to see what effect sizes are compatible with the test model at various consonance levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) , Schweder T, Hjort NL. (2002) , Singh K, Xie M, Strawderman WE. (2007) , Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) , Greenland S. (2019) , Chow ZR, Greenland S. (2019) , and Greenland S, Chow ZR. (2019) .", - "name": "concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions", + "description": "Allows one to compute consonance (confidence)\n intervals for various statistical tests along with their corresponding\n P-values, S-values, and likelihoods. The intervals can be plotted to\n create consonance, surprisal, and likelihood functions allowing one to\n see what effect sizes are compatible with the test model at various\n consonance levels rather than being limited to one interval estimate\n such as 95%. These methods are discussed by Poole C. (1987)\n , Schweder T, Hjort NL. (2002)\n , Singh K, Xie M, Strawderman WE. (2007)\n , Rothman KJ, Greenland S, Lash TL. (2008,\n ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019)\n , Greenland S. (2019)\n , Chow ZR, Greenland S. (2019)\n , and Greenland S, Chow ZR. (2019)\n .", + "name": "concurve: Computes and Plots Compatibility (Confidence) Intervals,\n P-Values, S-Values, & Likelihood Intervals to Form Consonance,\n Surprisal, & Likelihood Functions", "date": "2019-11-24", "codeRepository": "https://github.com/Zadchow/concurve", "relatedLink": [ @@ -57,51 +57,65 @@ "softwareSuggestions": [ { "@type": "SoftwareApplication", - "identifier": "testthat", - "name": "testthat", + "identifier": "covr", + "name": "covr", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=testthat" + "sameAs": "https://CRAN.R-project.org/package=covr" }, { "@type": "SoftwareApplication", - "identifier": "covr", - "name": "covr", + "identifier": "roxygen2", + "name": "roxygen2", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=covr" - } - ], - "softwareRequirements": [ + "sameAs": "https://CRAN.R-project.org/package=roxygen2" + }, { "@type": "SoftwareApplication", - "identifier": "parallel", - "name": "parallel" + "identifier": "spelling", + "name": "spelling", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=spelling" }, { "@type": "SoftwareApplication", - "identifier": "pbmcapply", - "name": "pbmcapply", + "identifier": "testthat", + "name": "testthat", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=pbmcapply" - }, + "sameAs": "https://CRAN.R-project.org/package=testthat" + } + ], + "softwareRequirements": [ { "@type": "SoftwareApplication", - "identifier": "compiler", - "name": "compiler" + "identifier": "bcaboot", + "name": "bcaboot", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=bcaboot" }, { "@type": "SoftwareApplication", @@ -117,27 +131,32 @@ }, { "@type": "SoftwareApplication", - "identifier": "bcaboot", - "name": "bcaboot", + "identifier": "compiler", + "name": "compiler" + }, + { + "@type": "SoftwareApplication", + "identifier": "dplyr", + "name": "dplyr", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=bcaboot" + "sameAs": "https://CRAN.R-project.org/package=dplyr" }, { "@type": "SoftwareApplication", - "identifier": "ProfileLikelihood", - "name": "ProfileLikelihood", + "identifier": "flextable", + "name": "flextable", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=ProfileLikelihood" + "sameAs": "https://CRAN.R-project.org/package=flextable" }, { "@type": "SoftwareApplication", @@ -153,87 +172,93 @@ }, { "@type": "SoftwareApplication", - "identifier": "metafor", - "name": "metafor", + "identifier": "knitr", + "name": "knitr", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=metafor" + "sameAs": "https://CRAN.R-project.org/package=knitr" }, { "@type": "SoftwareApplication", - "identifier": "dplyr", - "name": "dplyr", + "identifier": "metafor", + "name": "metafor", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=dplyr" + "sameAs": "https://CRAN.R-project.org/package=metafor" }, { "@type": "SoftwareApplication", - "identifier": "tidyr", - "name": "tidyr", + "identifier": "officer", + "name": "officer", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=tidyr" + "sameAs": "https://CRAN.R-project.org/package=officer" }, { "@type": "SoftwareApplication", - "identifier": "flextable", - "name": "flextable", + "identifier": "parallel", + "name": "parallel" + }, + { + "@type": "SoftwareApplication", + "identifier": "pbmcapply", + "name": "pbmcapply", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=flextable" + "sameAs": "https://CRAN.R-project.org/package=pbmcapply" }, { "@type": "SoftwareApplication", - "identifier": "officer", - "name": "officer", + "identifier": "ProfileLikelihood", + "name": "ProfileLikelihood", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=officer" + "sameAs": "https://CRAN.R-project.org/package=ProfileLikelihood" }, { "@type": "SoftwareApplication", - "identifier": "knitr", - "name": "knitr", + "identifier": "rlang", + "name": "rlang", + "version": ">= 0.1.2", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=knitr" + "sameAs": "https://CRAN.R-project.org/package=rlang" }, { "@type": "SoftwareApplication", - "identifier": "tibble", - "name": "tibble", + "identifier": "scales", + "name": "scales", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=tibble" + "sameAs": "https://CRAN.R-project.org/package=scales" }, { "@type": "SoftwareApplication", @@ -261,19 +286,49 @@ }, { "@type": "SoftwareApplication", - "identifier": "scales", - "name": "scales", + "identifier": "tibble", + "name": "tibble", "provider": { "@id": "https://cran.r-project.org", "@type": "Organization", "name": "Comprehensive R Archive Network (CRAN)", "url": "https://cran.r-project.org" }, - "sameAs": "https://CRAN.R-project.org/package=scales" + "sameAs": "https://CRAN.R-project.org/package=tibble" + }, + { + "@type": "SoftwareApplication", + "identifier": "tidyr", + "name": "tidyr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=tidyr" + }, + { + "@type": "SoftwareApplication", + "identifier": "magrittr", + "name": "magrittr", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=magrittr" + }, + { + "@type": "SoftwareApplication", + "identifier": "R", + "name": "R", + "version": ">= 3.2" } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "4102.335KB", + "fileSize": "3867.198KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", @@ -287,6 +342,7 @@ "citation": [ { "@type": "SoftwareSourceCode", + "datePublished": "2019", "author": [ { "@type": "Person", @@ -302,5 +358,6 @@ "name": "{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions", "url": "https://CRAN.R-project.org/package=concurve" } - ] + ], + "developmentStatus": "https://www.tidyverse.org/lifecycle/#maturing" } diff --git a/concurve.Rproj b/concurve.Rproj index f0d6187..5e0a049 100644 --- a/concurve.Rproj +++ b/concurve.Rproj @@ -1,15 +1,15 @@ Version: 1.0 -RestoreWorkspace: Default -SaveWorkspace: Default -AlwaysSaveHistory: Default +RestoreWorkspace: No +SaveWorkspace: No +AlwaysSaveHistory: No EnableCodeIndexing: Yes UseSpacesForTab: Yes NumSpacesForTab: 2 Encoding: UTF-8 -RnwWeave: Sweave +RnwWeave: knitr LaTeX: pdfLaTeX AutoAppendNewline: Yes @@ -19,3 +19,5 @@ BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source PackageRoxygenize: rd,collate,namespace,vignette + +QuitChildProcessesOnExit: Yes diff --git a/cran-comments.md b/cran-comments.md index e69de29..2ddff8f 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -0,0 +1,12 @@ +## Test environments + +* local: darwin15.6.0-3.6.1 +* travis: 3.1, 3.2, 3.3, oldrel, release, devel +* r-hub: windows-x86_64-devel, ubuntu-gcc-release, fedora-clang-devel +* win-builder: windows-x86_64-devel + +## R CMD check results + +0 errors | 0 warnings | 1 note + +* This is a new release. diff --git a/data/CRAN_Packages.RData b/data/CRAN_Packages.RData deleted file mode 100644 index 5907e895448700f3d309f6b9d5652331da28b57d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 45174 zcmbqccUY6x_wTS;>%c+<8G;B%5h2PH8L1+IB1Tb5nW?e`L_kIuNh>NvHYgDgQnrYY zq7XKz6~ZPWLLgC4AOuB55?1!_1_TxP{_6A8Ki>D;bIQnZ7YqNs z9#3C)=Vo`eAR=oA?o>(rjqTFbryPx4*EAG=J1hFjuPVo^0}Fa`1AhsQ7_U9vr?^W> zY1b~ow(w5HBQfVEZ`3L#2-G>Xk0<&J{U$XvqiQAv=|oq9=d{Z4=YB|9;6zx4H3uHb zl(fG%G00>cm_P8gzk_tHoQ+}!RKH@^<);rZ{XOAuH`>@JB|Gy_-1iD^ApUm+G*m-T zfbp1sImw=!n;CeK)?ZP?ht6nIyghEVduKE@gh_q(dyx?{(tblgj6DZwx^8cdA z9-(Zst==SgDk@p4UtU`8TE z`fGx7ZX8qlP_Vw1;})$ankO}%95a?OOW!>q-M7j8&mTDnjQyP*cF==%x|uJxoieg3 z)#Np^QUiREdFiidTTV@Glc)N3#XZYTtGGD5Z)kq2;pXpW+7Eb)xfgRxSX)lP#z<`s zJ(v%O^Cqr71JQneR9qxz^CZI#sFK36YGF-7^X?a$F2!zn_aSLY{(kJyw1rA4Y(*1-Zz)g6`uSMctZFL{bfo z8%meVVVMBr#3~qr2XuaF8a)>{0?W)Xi(H@!Moi{QM^6 zK}tk)_CMaCiKaQwW2o1=iall-8wywtt*!Jj#C4B{L@JzyMYTR1pz^lA_8sH3`rn$% zMfzqWy2kV$fozI05 zN?CQ47cI{vS*G12X0m2JO~^9;Ox4lQ@~RitdeZZc`{Pf$vh^H6`cuDu()t> z#IdJ!8ddOPt517apE~aBmZ2t=bZwh>{Y{AIOnc9x-dt=2Dy3$i29@$A_NZL?xu>KP zcAhBa>yg>1`z1q~DsNBWJ7v%bhQ*Dq8HZr%Z&UPebY zEA9q3_tzb}?C_Fq#1TYDpK@Mrh7Moe(KM#TtygrT%z*&z%TtmrPkQ|aX0-+`zwrrw z(m<++V`vKkAD=nkEmoUiVdhxS)q6iv(pu8Eoe?dg>GW(qQ}XM^9E$e2I5AVP2?NB5Fo?ZiM6Pr4GN zFy}qYw|I_^yl>VGIjlcalKAaPHNw9fBaFYY2@?n*N(-R`Y{GZ-k2Kj$pG<&m2>S_-E zPlFuy^4wE-T}J7%uS;S1VF$*$+%YFOm&qb|yk9@LWb(F5bOyC#i3>B6m+6kH_W z|4k&G=k6hV&c%i6Ng)3FMXqsvXd6mqD9(}V6Ag`}H}2!_C#}A4HH_O^Nw?UThoct0CC0-1X7W-|5PG`D+NPE1TxUBvCtuU{2h~XlMdmS%ZI= zfLT>pKcBP>B{~E<&pjFqjif8@<0}#{t18>(lO$0ZLogR^b2K!CuB^#-B4Aci;v?vj z=eQx;1hRyG7t!=dXKsk3z=44OS22!0>B!}56BrTj|0-hWldfEjqyS04|C@;VjV|{q zcilF@mxO;8 zzau+R0y`8HnZ%Ts!*O@cnoT#W7-v~^7@JMEB19uI^dLJrb!qlFot(5Idf)K!{}lW% zjk&@<{RH(L8+K5jG79~V%B{ToEWw#+%nknOPSkET%tTNy3jL4DO}zX}fz~wUI=|}) zN{S6L7PyZ>|D$p{FF#vwc^VVT@9IPyV8cuWuScQ(QTa8`^-saZY0U3@%_pd}Y~oJ> z$x-NkREqIjvjoSdF_C=DPSh?o@vtCe6#5^P8+fjnf<4oiYy5{#P~vRjA%XQM^gk-4 zd9K+4&uL5y|6wOehfO>p=o^LpN98&m?N5QsG$x$y_XM?>O*|;j8-@Nyr38u zMDhJPQR-}>iJ)>6`X7}dJX)r}U>Xy_XFfs6vWdn5#3=MXD&=^zY(eNWCXUbSL>aM( zrUKR|^uH~()}v6an&{tbDAUXymW^;q z_l23{$rBGhxL8>me{i_LtY_ccZYSvQj9HKNT)^;-;ggDEcl5tYiIj*FwA9`h-Jz9&=O3IBeA(G{X*~2ia9KYK zzrsV$1Kpx@8N{Hp&`7*q&o}=|Ao>=s_{WlzBTVX%P@Q-X#IRD57f;j z%P2eA<*vRAW3cQjzbvIK!(2SF1XK0RlJo1Imkf`T zy-}KlU+1CE37nBYxhTB=-jWNAT=^1###{VG4rIg5C20&~)%urE?YRX6@mSb+aD3TW z4@?QL2Wnq1vx_j)_b|&61b5R43^swR z90k-~6k^M=1O?H=E~vvnDtZeIR{*#OLp*q1Xb;pGQOhtQ%g$i)E&{T4EyJXNR2E^p zm!0djE*V|}&Q`w)w)Uc~$t_7Hy1@DO1+_a!>MIWRZ@%i4Wr+=v`ffqJ4a|e;U&6*t zv1RAYq6_wa32xXe!yuNO4=qbN%P_ssOEBj*EISu18-@W1sDTY{S*ixk#}?Gb!Lhm3 zivn9g^6-+>CroOgLEQmjSZt&$JNto&1@=Ha0w#75rtLip*u0B?mFt&bT$W*Mmz^(y zleS>!eJ0=Gyx?pvYU?bVYPuwKgVnV57a_ZT!ayl;-a&V z6c;5SDK5g)0#aOrnGurWqO*_`7Ys>4Qrt8P59g7Dq_`*vNl|;Dkpzy7$6o@exAi|*|+V`To04ctM5t8Dfvyc=QB_Sy;!U#!m5vCfD;-d4okQ5gTk7x@?F@i@rC-4`N z;-VxZ#qA3XAt@esi{H$_3Q2KM5|ZNBU&6*k;l6q-Y*YzJanV^wiohPIg`~I$BP7Ll zFw2sV6yL!BQhWy^B*jH%At^4p2uU&cEnb#`6_VnjBqYVLg@zG0HY7*EOfM=7km5T@ zNQ!X_4IwESE;NLsxace-MPLuq7(j~eU;qWYa|WAtQQ`?naS=vHiiqQU_wE=tCL6bF7=Xb4I1OOE&7d}$#mE=odDT)WT^lA`oq!iGE`#dpqt z6ovMG39bWDd

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    + + + + + + + + + + + diff --git a/docs/articles/examples.html b/docs/articles/examples.html new file mode 100644 index 0000000..ca289e2 --- /dev/null +++ b/docs/articles/examples.html @@ -0,0 +1,324 @@ + + + + + + + +Examples in R • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    + + + + +
    +

    +Using Mean Differences

    +

    If we were to generate some random data from a normal distribution with the following code,

    + +

    and compare the means of these two “groups” using a t-test,

    +
    testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE)
    +

    we would likely see some differences, given that we have such a small sample in each group.

    +
    summary(testresults)
    +

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

    +

    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

    +

    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

    +

    It is relatively easy to produce such a function using the concurve package in R.

    +

    Install the package directly from CRAN

    +
    install.packages("concurve")
    +

    or get a more slightly up-to-date version via GitHub.

    +
    library(devtools)
    +install_github("zadchow/concurve")
    +

    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    + +

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    +
    tibble::tibble(intervalsdf)
    +

    There’s a quick look at the first few entries of our dataframe.

    +

    We can plot this data using the ggconcurve() function.

    +
    pfunction <- ggconcurve(type = "consonance", intervalsdf)
    +pfunction
    +
    + +
    +

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    +

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    +
    sfunction <- ggconcurve(type = "surprisal", intervalsdf)
    +sfunction
    +
    + +
    +

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    +
    +
    +

    +Simple Linear Models

    +

    We can also try this with other simple linear models.

    +

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    +
    GroupA2 <- rnorm(500)
    +GroupB2 <- rnorm(500)
    +
    +RandomData2 <- data.frame(GroupA2, GroupB2)
    +
    +model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    +
    +summary(model)
    +

    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    +
    randomframe <- curve_gen(model, "GroupB2")
    +tibble::tibble(randomframe)
    +

    Now that we have our data frame, we can graph our function.

    +
    ggconcurve(type = "consonance", randomframe)
    +
    + +
    +
    s <- ggconcurve(type = "surprisal", randomframe)
    +s
    +
    + +
    +

    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    + +

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    + +
    + +
    +

    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    +
    +
    +

    +Using Meta-Analysis Data

    +

    Here we present another quick example with a meta-analysis of simulated data.

    +

    First, we generate random data for two groups in two hypothetical studies

    +
    GroupAData <- runif(20, min = 0, max = 100)
    +GroupAMean <- round(mean(GroupAData), digits = 2)
    +GroupASD <- round(sd(GroupAData), digits = 2)
    +
    +GroupBData <- runif(20, min = 0, max = 100)
    +GroupBMean <- round(mean(GroupBData), digits = 2)
    +GroupBSD <- round(sd(GroupBData), digits = 2)
    +
    +GroupCData <- runif(20, min = 0, max = 100)
    +GroupCMean <- round(mean(GroupCData), digits = 2)
    +GroupCSD <- round(sd(GroupCData), digits = 2)
    +
    +GroupDData <- runif(20, min = 0, max = 100)
    +GroupDMean <- round(mean(GroupDData), digits = 2)
    +GroupDSD <- round(sd(GroupDData), digits = 2)
    +

    We can then quickly combine the data in a dataframe.

    +
    StudyName <- c("Study1", "Study2")
    +MeanTreatment <- c(GroupAMean, GroupCMean)
    +MeanControl <- c(GroupBMean, GroupDMean)
    +SDTreatment <- c(GroupASD, GroupCSD)
    +SDControl <- c(GroupBSD, GroupDSD)
    +NTreatment <- c(20, 20)
    +NControl <- c(20, 20)
    +
    +metadf <- data.frame(
    +  StudyName, MeanTreatment, MeanControl,
    +  SDTreatment, SDControl,
    +  NTreatment, NControl
    +)
    +

    Then, we’ll use metafor to calculate the standardized mean difference.

    + +

    Next, we’ll pool the data using a fixed-effects common-effects model

    + +

    Let’s look at our output.

    + +
    Fixed-Effects Model (k = 2)
    +
    +Test for Heterogeneity: 
    +Q(df = 1) = 0.61, p-val = 0.44
    +
    +Model Results:
    +
    +estimate    se  zval  pval  ci.lb  ci.ub 
    +    0.16  0.22  0.73  0.47  -0.28   0.60  
    +
    +---
    +Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    +

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    +

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    + +

    Now that we have our dataframe with every computed interval, we can plot the functions.

    +
    ggconcurve(type = "consonance", metaf)
    +
    + +
    +

    And our S-value function

    +
    ggconcurve(type = "surprisal", metaf)
    +
    + +
    +

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

    +
    +
    + + + +
    + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + +

    + + + + + + + + + + + diff --git a/docs/articles/stata.html b/docs/articles/stata.html new file mode 100644 index 0000000..37c5ef4 --- /dev/null +++ b/docs/articles/stata.html @@ -0,0 +1,302 @@ + + + + + + + +Using Stata • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    + + + + +

    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

    +

    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

    + +

    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

    +

    First, let’s visualize the data with a scatterplot.

    + +
    + +
    +


    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

    +

    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

    + +
    + +
    +


    That’s what our output looks like.

    +

    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

    +
    + +
    +


    +

    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

    +

    Here’s the code that we’ll be using to achieve that.

    + +

    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

    + +

    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

    +
      +
    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

    • +
    • pvalue”is computed by taking 1 - “level”, which is alpha.

    • +
    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

    • +
    • my_new_data” is the filename that we’ve assigned to our new dataset.

    • +
    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

    • +
    +

    Here are the next few major lines

    + +

    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

    +

    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

    +

    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

    +

    The next line two lines

    + +

    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

    + +

    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

    +
    + +
    +


    +

    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

    +

    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

    +

    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

    +

    “post topost” is writing the results from each loop as new observations in this data structure.

    +

    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

    + +

    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

    + +

    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

    +

    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

    +

    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

    +

    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

    +

    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

    + +

    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

    +
    + +
    +


    +

    So now, here’s what our consonance function looks like.

    +
    + +
    +


    And here’s what our surprisal function looks like.

    +
    + +
    +


    It looks slighly better when the y-axis is transformed logarithmically…

    +
    + +
    +


    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

    +

    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

    +

    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

    +

    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

    + +

    We simply replace the first line within the loop with our intended command, just as I’ve replaced

    + +

    with

    + +

    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

    + +

    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

    +
    + + + +
    + + + + +
    + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 0000000..660c3b9 --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,218 @@ + + + + + + + + +Citation and Authors • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +

    Chow Z, Vigotsky A (2019). +concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. +https://CRAN.R-project.org/package=concurve. +

    +
    @Manual{,
    +  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
    +  author = {Zad R. Chow and Andrew D. Vigotsky},
    +  year = {2019},
    +  url = {https://CRAN.R-project.org/package=concurve},
    +}
    + + + + + +
    + +
    + + + + +
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+ padding: .1rem 1rem; + margin-bottom: 0.1; + font-size: 1.0em; + font-weight: 400 + /* display: none */ +} + +.algolia-autocomplete .algolia-docsearch-suggestion--title { + display: block; + padding: .25rem 1rem; + margin-bottom: 0; + font-size: 0.9em; + font-weight: 400 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--text { + padding: 0 1rem .5rem; + margin-top: -.25rem; + font-size: 0.8em; + font-weight: 400; + line-height: 1.25 +} + +.algolia-autocomplete .algolia-docsearch-footer { + width: 110px; + height: 20px; + z-index: 3; + margin-top: 10.66667px; + float: right; + font-size: 0; + line-height: 0; +} + +.algolia-autocomplete .algolia-docsearch-footer--logo { + background-image: url("data:image/svg+xml;utf8,"); + background-repeat: no-repeat; + background-position: 50%; + background-size: 100%; + overflow: hidden; + text-indent: -9000px; + width: 100%; + height: 100%; + display: block; + transform: translate(-8px); +} + +.algolia-autocomplete .algolia-docsearch-suggestion--highlight { + color: #FF8C00; + background: rgba(232, 189, 54, 0.1) +} + + +.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { + box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) +} + +.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { + background-color: rgba(192, 192, 192, .15) +} diff --git a/docs/docsearch.js b/docs/docsearch.js new file mode 100644 index 0000000..b35504c --- /dev/null +++ b/docs/docsearch.js @@ -0,0 +1,85 @@ +$(function() { + + // register a handler to move the focus to the search bar + // upon pressing shift + "/" (i.e. "?") + $(document).on('keydown', function(e) { + if (e.shiftKey && e.keyCode == 191) { + e.preventDefault(); + $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/docsearch.json b/docs/docsearch.json new file mode 100644 index 0000000..aa1fc65 --- /dev/null +++ b/docs/docsearch.json @@ -0,0 +1,95 @@ +{ + "index_name": "lesslikely-concurve", + "start_urls": [ + { + "url": "https://data.lesslikely.com/concurve//index.html", + "selectors_key": "homepage", + "tags": [ + "homepage" + ] + }, + { + "url": "https://data.lesslikely.com/concurve//reference", + "selectors_key": "reference", + "tags": [ + "reference" + ] + }, + { + "url": "https://data.lesslikely.com/concurve//articles", + "selectors_key": "articles", + "tags": [ + "articles" + ] + } + ], + "stop_urls": [ + "/reference/$", + "/reference/index.html", + "/articles/$", + "/articles/index.html" + ], + "sitemap_urls": [ + "https://data.lesslikely.com/concurve//sitemap.xml" + ], + "selectors": { + "homepage": { + "lvl0": { + "selector": ".contents h1", + "default_value": "concurve Home page" + }, + "lvl1": { + "selector": ".contents h2" + }, + "lvl2": { + "selector": ".contents h3", + "default_value": "Context" + }, + "lvl3": ".ref-arguments td, .ref-description", + "text": ".contents p, .contents li, .contents .pre" + }, + "reference": { + "lvl0": { + "selector": ".contents h1" + }, + "lvl1": { + "selector": ".contents .name", + "default_value": "Argument" + }, + "lvl2": { + "selector": ".ref-arguments th", + "default_value": "Description" + }, + "lvl3": ".ref-arguments td, .ref-description", + "text": ".contents p, .contents li" + }, + "articles": { + "lvl0": { + "selector": ".contents h1" + }, + "lvl1": { + "selector": ".contents .name" + }, + "lvl2": { + "selector": ".contents h2, .contents h3", + "default_value": "Context" + }, + "text": ".contents p, .contents li, .tempate-article .contents .pre" + } + }, + "selectors_exclude": [ + ".dont-index" + ], + "min_indexed_level": 2, + "custom_settings": { + "separatorsToIndex": "_", + "attributesToRetrieve": [ + "hierarchy", + "content", + "anchor", + "url", + "url_without_anchor" + ] + } +} + diff --git a/docs/extra.css b/docs/extra.css new file mode 100644 index 0000000..0f1be09 --- /dev/null +++ b/docs/extra.css @@ -0,0 +1,98 @@ +html, body { + height: 100%; + font-size: 18px; + font-family: "PT Serif"; +} + +.navbar-brand { + font-size: 16px; + font-weight: bold; +} + +.navbar-default .navbar-nav>li>a:hover, +.navbar-default .navbar-nav>li>a:focus { + background-color: #eee; +} + +.nav-pills>li.active>a, +.nav-pills>li.active>a:hover, +.nav-pills>li.active>a:focus { + background-color:#d46c5b; +} + +.label-default { + background-color:#d46c5b; +} + +p.caption { + display: none; +} + +body { + color: #444444; + line-height: 1.6; +} + +a { + color: #d46c5b; +} + +a:focus { + color: #d46c5b; +} + +h1, .h1 { + font-size: 32px; +} + +h1 small, .h1 small { + font-size: 70%; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +h2, .h2 { + font-size: 28px; +} + +h3, .h3 { + font-size: 20px; +} + +h4, .h4 { + font-size: 20px; +} + +.contents h1, .contents h2, .contents h3, .contents h4 { + margin-bottom: 20px; +} + +.contents .page-header h1 { + margin-top: 25px; +} + +.contents h3, .contents h4 { + margin-top: -55px; +} + +h4.date, h4.author { + font-size: 1em; + color: #bbb; + display: inline; + padding-right: 0.5em; +} + +pre code { + font-size: 13.5px; + white-space: pre; +} + +.btn-primary { + color: #eeeeee; + background-color: #d46c5b; +} + +.btn-primary:focus { + color: #eeeeee; + background-color: #d46c5b; +} diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..30e11c2 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,334 @@ + + + + + + + +Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
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    + +
    + + + +
    +
    +
    +

    +Examples

    +
    +
    +

    +Plot Publication-Ready Interval Functions

    +
    +

    +(Consonance, Surprisal, Likelihood, & Deviance)

    +

    +
    +
    +
    +
    +

    +Compare Interval Functions From Different Data/Studies

    +

    +
    +
    +
    +

    +Export Tabled Results Easily For Word, Powerpoint, & LaTeX

    +

    +
    +
    +
    +

    +Installation

    +
    +

    +For R:

    +
    +

    +Install the Package From CRAN

    +
    install.packages("concurve")
    +
    +
    +

    +Install the Developer Version

    +
    library(devtools)
    +install_github("zadchow/concurve")
    +
    +
    +

    +Check out the Examples.

    +
    +
    +
    +
    +

    +For Stata:

    +
    +

    +Check out the Article on Using Stata for concurve.

    +
    +
    +
    +
    +
    +

    +Dependencies

    +
      +
    • ggplot2
    • +
    • metafor
    • +
    • parallel
    • +
    • dplyr
    • +
    • tidyr
    • +
    • knitr
    • +
    • flextable
    • +
    • officer
    • +
    • tibble
    • +
    • survival
    • +
    • survminer
    • +
    • scales
    • +
    +
    +
    +
    +

    +Purpose

    +
    +

    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

    +

    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

    +

    Following the common (and important) warning that P-values are not hypothesis probabilities, we caution that the P-value graph is not a probability distribution: It shows compatibility of parameter values with the data, rather than plausibility or probability of those values given the data. This is not a subtle difference: compatibility is a much weaker condition than plausibility. Consider for example that complete fabrication of the data is always an explanation compatible with the data (and indeed has happened in some influential medical studies [65]), but in studies with many participants and authors involved in all aspects of data collection it becomes so implausible or improbable as to not even merit mention. We emphasize then that all the P-value ever addresses in a direct logical sense is compatibility; for hypothesis probabilities one must turn to Bayesian methods [25].

    + +
    +
    +
    +

    In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

    +

    From a scientific perspective, the likelihood function and p-value function provide the basis for scientific judgments by an investigator, and by other investigators who might have interest. It thus replaces a blunt yes or no decision by an opportunity for appropriate informed judgment.” - Fraser, 2019

    +
    +
    +
    +

    Statistical software does not help you know what to compute, nor how to interpret the result. It does not offer to explain the assumptions behind methods, nor does it flag delicate or dubious assumptions. It does not warn you about multiplicity or p-hacking. It does not check whether you picked the hypothesis or analysis after looking at the data, nor track the number of analyses you tried before arriving at the one you sought to publish – another form of multiplicity. The more “powerful” and “user-friendly” the software is, the more it invites cargo-cult statistics." - Stark & Saltelli, 2018

    +
    +
    +
    +

    +References

    +
      +
    1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:1909.08579 [stat.ME]. 2019
    2. +
    3. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics. arXiv:1909.08583 [stat.ME]. 2019
    4. +
    5. Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.
    6. +
    7. Sullivan KM, Foster DA. Use of the confidence interval function._Epidemiology._ 1990;1(1):39-42.
    8. +
    9. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012.
    10. +
    11. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv [mathST]. 2007.
    12. +
    13. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332.
    14. +
    15. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There is No Replication Crisis if We Don’t Expect Replication. Am Stat. 2019
    16. +
    17. Greenland S. Valid P-values Behave Exactly As They Should. Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2019;18(136).
    18. +
    19. Fraser DAS. The p-value Function and Statistical Inference. Am Stat. 2019
    20. +
    21. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15(4):40-43.
    22. +
    +
    + + +
    + + +
    + + + +
    + + + + + diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 0000000..88ad827 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/logo.svg b/docs/logo.svg new file mode 100644 index 0000000..f42ed9b --- /dev/null +++ b/docs/logo.svg @@ -0,0 +1,93 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/news/index.html b/docs/news/index.html new file mode 100644 index 0000000..854ed0f --- /dev/null +++ b/docs/news/index.html @@ -0,0 +1,303 @@ + + + + + + + + +Changelog • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +concurve 2.2.0 Unreleased +

    +
    +
    +

    +concurve 2.1.0 2019-09-19 +

    +
    +

    +Major changes

    +
      +
    • +ggconcurve() now plots both the P-values and CI level using both y-axes when the type = “consonance”. Previously, this was only possible via plot_concurve() (which uses base R graphics) because ggplot2 had a bug in its last few versions, which inhibited proper transformations in the y-axis.
    • +
    +
    +
    +
    +

    +concurve 2.0.1 Unreleased +

    +
    +

    +Major changes

    +
      +
    • +plot_concurve() now has “measure” as an item which allows for ratio measures to be logarithmically scaled on the x axis. There are two options, “default”, which is set as the default option and is for mean differences, and “ratio”, which will result in the axis being logarithmically scaled.
    • +
    • +plot_concurve() also now has a “fill” option which will allow users to choose the color of the plot.
    • +
    +
    +
    +
    +

    +concurve 2.0 2019-07-10 +

    +
    +

    +Major changes

    +
      +
    • The plotpint() function which plotted consonance functions has been repackaged into ggconcurve().
    • +
    • The plotsint() function which plotted surprisal functions has been repackaged into ggconcurve().
    • +
    • Functions can now also be plotted with base R via the plot_concurve() function.
    • +
    • Consonance functions can be plotted as a pyramid (right side up) or inverted (upside down) via the “position” item in ggconcurve().
    • +
    • Null values (for means & ratios) can be plotted via the ggconcurve() function to show how much of the interval surrounds it.
    • +
    • Log transformations included in all the plotting functions for ratio measures.
    • +
    • Parallel programming has now been implemented into the computations via the mclapply() function from the parallel package.
    • +
    +
    +
    +
    +

    +concurve 1.08 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Can produce consonance and surprisal functions for correlations via the corrintervals() function.
    • +
    • Now able to construct consonance and surprisal functions from the point estiate, and confidence limits via the rev_eng() function.
    • +
    • Graphs produced via the plotpint() or plotsint() function now able to take custom titles, subtitles, x-axis titles, and captions.
    • +
    +
    +
    +
    +

    +concurve 1.07 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Can now produce consonance and surprisal functions for survival data produced with the survival package.
    • +
    +
    +
    +
    +

    +concurve 1.06 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Now contains documentation for producing interval functons in Stata.
    • +
    +
    +
    +

    +Minor changes

    +
      +
    • Default plots now contain grids, title, subtitle, and a caption.
    • +
    • Updated figures in README and the ‘Examples in R’ vignette/article.
    • +
    +
    +
    +
    + + + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/pkgdown.css b/docs/pkgdown.css new file mode 100644 index 0000000..9145958 --- /dev/null +++ b/docs/pkgdown.css @@ -0,0 +1,256 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; + color: #666; + display: flex; + flex-shrink: 0; +} +footer p { + margin-bottom: 0; +} +footer div { + flex: 1; +} +footer .pkgdown { + text-align: right; +} +footer p { + margin-bottom: 0; +} + +img.icon { + float: right; +} + +img { + max-width: 100%; +} + +/* Fix bug in bootstrap (only seen in firefox) */ +summary { + display: list-item; +} + +/* Typographic tweaking ---------------------------------*/ + +.contents .page-header { + margin-top: calc(-60px + 1em); +} + +/* Section anchors ---------------------------------*/ + +a.anchor { + margin-left: -30px; + display:inline-block; + width: 30px; + height: 30px; + visibility: hidden; + + background-image: url(./link.svg); + background-repeat: no-repeat; + background-size: 20px 20px; + background-position: center center; +} + +.hasAnchor:hover a.anchor { + visibility: visible; +} + +@media (max-width: 767px) { + .hasAnchor:hover a.anchor { + visibility: hidden; + } +} + + +/* Fixes for fixed navbar --------------------------*/ + +.contents h1, .contents h2, .contents h3, .contents h4 { + padding-top: 60px; + margin-top: -40px; +} + +/* Sidebar --------------------------*/ + +#sidebar { + margin-top: 30px; + position: -webkit-sticky; + position: sticky; + top: 70px; +} +#sidebar h2 { + font-size: 1.5em; + margin-top: 1em; +} + +#sidebar h2:first-child { + margin-top: 0; +} + +#sidebar .list-unstyled li { + margin-bottom: 0.5em; +} + +.orcid { + height: 16px; + /* margins are required by official ORCID trademark and display guidelines */ + margin-left:4px; + margin-right:4px; + vertical-align: middle; +} + +/* Reference index & topics ----------------------------------------------- */ + +.ref-index th {font-weight: normal;} + +.ref-index td {vertical-align: top;} +.ref-index .icon {width: 40px;} +.ref-index .alias {width: 40%;} +.ref-index-icons .alias {width: calc(40% - 40px);} +.ref-index .title {width: 60%;} + +.ref-arguments th {text-align: right; padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + +/* Syntax highlighting ---------------------------------------------------- */ + +pre { + word-wrap: normal; + word-break: normal; + border: 1px solid #eee; +} + +pre, code { + background-color: #f8f8f8; + color: #333; +} + +pre code { + overflow: auto; + word-wrap: normal; + white-space: pre; +} + +pre .img { + margin: 5px 0; +} + +pre .img img { + background-color: #fff; + display: block; + height: auto; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.message { color: black; font-weight: bolder;} +.error { color: orange; font-weight: bolder;} +.warning { color: #6A0366; font-weight: bolder;} + +/* Clipboard --------------------------*/ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; +} + +/* headroom.js ------------------------ */ + +.headroom { + will-change: transform; + transition: transform 200ms linear; +} +.headroom--pinned { + transform: translateY(0%); +} +.headroom--unpinned { + transform: translateY(-100%); +} + +/* mark.js ----------------------------*/ + +mark { + background-color: rgba(255, 255, 51, 0.5); + border-bottom: 2px solid rgba(255, 153, 51, 0.3); + padding: 1px; +} + +/* vertical spacing after htmlwidgets */ +.html-widget { + margin-bottom: 10px; +} + +/* fontawesome ------------------------ */ + +.fab { + font-family: "Font Awesome 5 Brands" !important; +} + +/* don't display links in code chunks when printing */ +/* source: https://stackoverflow.com/a/10781533 */ +@media print { + code a:link:after, code a:visited:after { + content: ""; + } +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js new file mode 100644 index 0000000..087a762 --- /dev/null +++ b/docs/pkgdown.js @@ -0,0 +1,113 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('.navbar-fixed-top').headroom(); + + $('body').css('padding-top', $('.navbar').height() + 10); + $(window).resize(function(){ + $('body').css('padding-top', $('.navbar').height() + 10); + }); + + $('body').scrollspy({ + target: '#sidebar', + offset: 60 + }); + + $('[data-toggle="tooltip"]').tooltip(); + + var cur_path = paths(location.pathname); + var links = $("#navbar ul li a"); + var max_length = -1; + var pos = -1; + for (var i = 0; i < links.length; i++) { + if (links[i].getAttribute("href") === "#") + continue; + // Ignore external links + if (links[i].host !== location.host) + continue; + + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
    +
    + + + + +
    + +
    +
    + + +
    +

    All vignettes

    +

    + + +
    +
    +
    + + + +
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $(".examples, div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 0000000..c4bf0d4 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,10 @@ +pandoc: '2.6' +pkgdown: 1.4.1 +pkgdown_sha: ~ +articles: + examples: examples.html + stata: stata.html +urls: + reference: https://data.lesslikely.com/concurve//reference + article: https://data.lesslikely.com/concurve//articles + diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html new file mode 100644 index 0000000..bef7a09 --- /dev/null +++ b/docs/reference/curve_compare.html @@ -0,0 +1,260 @@ + + + + + + + + +Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets — curve_compare • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    curve_compare(data1, data2, type = "consonance", plot = TRUE, ...)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    data1

    data2

    type

    plot

    ...

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html new file mode 100644 index 0000000..2010f2b --- /dev/null +++ b/docs/reference/curve_corr.html @@ -0,0 +1,258 @@ + + + + + + + + +Computes consonance intervals for correlations — curve_corr • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes consonance intervals to produce P- and S-value functions for correlational analyses +using the cor.test function in base R and places the interval limits for each interval level +into a data frame along with the corresponding p-values and s-values.

    +
    + +
    curve_corr(x, y, alternative, method, steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    x

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    y

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    alternative

    Indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.

    method

    A character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    +GroupA <- rnorm(50) +GroupB <- rnorm(50) + +joe <- curve_corr(x = GroupA, y = GroupB, + alternative = "two.sided", method = "pearson") + +tibble::tibble(joe)
    #> # A tibble: 3 x 1 +#> joe +#> <named list> +#> 1 <df[,7] [10,000 × 7]> +#> 2 <df[,1] [19,999 × 1]> +#> 3 <df[,7] [9 × 7]>
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html new file mode 100644 index 0000000..f378826 --- /dev/null +++ b/docs/reference/curve_gen.html @@ -0,0 +1,261 @@ + + + + + + + + +Computes consonance intervals for linear models — curve_gen • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the +selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits +for each interval level into a data frame along with the corresponding p-values and s-values.

    +
    + +
    curve_gen(model, var, method = "default", replicates = 1000, steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    model

    The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here.

    var

    The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.

    method

    Chooses the method to be used to calculate the consonance intervals. There are currently four +methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to +compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically +what most people are familiar with when computing intervals based on the calculated standard error. +The "lm" method allows this function to be used for specific scenarios like logistic regression and +the 'glm' function. The "boot" method allows for bootstrapping at certain levels.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 compatibility intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    +# Simulate random data + +GroupA <- rnorm(50) +GroupB <- rnorm(50) + +RandomData <- data.frame(GroupA, GroupB) + +rob <- glm(GroupA ~ GroupB, data = RandomData) +bob <- curve_gen(rob, "GroupB", method = "lm")
    #> Error in do.call(rbind, results): object 'results' not found
    +tibble::tibble(bob)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'bob' not found
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html new file mode 100644 index 0000000..8e37f4b --- /dev/null +++ b/docs/reference/curve_lik.html @@ -0,0 +1,270 @@ + + + + + + + + +Computes Likelihood Intervals for Ratio Data +<!-- %% ~~function to do ... ~~ --> — curve_lik • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    curve_lik(point, LL, UL, measure = "ratio", steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    point

    LL

    UL

    measure

    steps

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    +tibble::tibble(lik1)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'lik1' not found
    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    +tibble::tibble(lik2)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'lik2' not found
    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    #> NULL
    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
    #> NULL
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html new file mode 100644 index 0000000..3a2e347 --- /dev/null +++ b/docs/reference/curve_mean.html @@ -0,0 +1,273 @@ + + + + + + + + +Computes consonance intervals for mean differences — curve_mean • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test +that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values.

    +
    + +
    curve_mean(x, y, data, paired = F, method = "default",
    +replicates = 1000, steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    x

    Variable that contains the data for the first group being compared.

    y

    Variable that contains the data for the second group being compared.

    data

    Data frame from which the variables are being extracted from.

    paired

    Indicates whether the statistical test is a paired difference test. By default, it is set to "F", +which means the function will be an unpaired statistical test comparing two independent groups. +Inserting "paired" will change the test to a paired difference test.

    method

    By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted +into the function call.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more +consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended +as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    +# Simulate random data + +GroupA <- runif(100, min = 0, max = 100) +GroupB <- runif(100, min = 0, max = 100) + +RandomData <- data.frame(GroupA, GroupB) + +bob <- curve_mean(GroupA, GroupB, RandomData) + +tibble::tibble(bob)
    #> # A tibble: 3 x 1 +#> bob +#> <named list> +#> 1 <df[,7] [10,000 × 7]> +#> 2 <df[,1] [19,999 × 1]> +#> 3 <df[,7] [9 × 7]>
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html new file mode 100644 index 0000000..66be965 --- /dev/null +++ b/docs/reference/curve_meta.html @@ -0,0 +1,298 @@ + + + + + + + + +Computes consonance intervals for meta-analysis data — curve_meta • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the +meta-analysis done by the metafor package and places the interval limits for each interval level +into a data frame along with the corresponding p-values and s-values.

    +
    + +
    curve_meta(x, measure = "default", steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + +
    x

    Object where the meta-analysis parameters are stored, typically a list produced by 'metafor'

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default. +If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more +consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended +as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3). +https://www.jstatsoft.org/article/view/v036i03/v36i03.pdf.

    +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    +# Simulate random data for two groups in two studies + +GroupAData <- runif(20, min = 0, max = 100) +GroupAMean <- round(mean(GroupAData), digits = 2) +GroupASD <- round(sd(GroupAData), digits = 2) + +GroupBData <- runif(20, min = 0, max = 100) +GroupBMean <- round(mean(GroupBData), digits = 2) +GroupBSD <- round(sd(GroupBData), digits = 2) + +GroupCData <- runif(20, min = 0, max = 100) +GroupCMean <- round(mean(GroupCData), digits = 2) +GroupCSD <- round(sd(GroupCData), digits = 2) + +GroupDData <- runif(20, min = 0, max = 100) +GroupDMean <- round(mean(GroupDData), digits = 2) +GroupDSD <- round(sd(GroupDData), digits = 2) + +# Combine the data + +StudyName <- c("Study1", "Study2") +MeanTreatment <- c(GroupAMean, GroupCMean) +MeanControl <- c(GroupBMean, GroupDMean) +SDTreatment <- c(GroupASD, GroupCSD) +SDControl <- c(GroupBSD, GroupDSD) +NTreatment <- c(20, 20) +NControl <- c(20, 20) + +metadf <- data.frame( + StudyName, MeanTreatment, MeanControl, + SDTreatment, SDControl, NTreatment, NControl +) + +# Use metafor to calculate the standardized mean difference + +library(metafor)
    #> Loading required package: Matrix
    #> Loading 'metafor' package (version 2.1-0). For an overview +#> and introduction to the package please type: help(metafor).
    +dat <- escalc( + measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, + m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf +) + +# Pool the data using a particular method. Here "FE" is the fixed-effects model + +res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) + +# Calculate the intervals using the metainterval function + +metaf <- curve_meta(res) + +tibble::tibble(metaf)
    #> # A tibble: 3 x 1 +#> metaf +#> <named list> +#> 1 <df[,7] [10,000 × 7]> +#> 2 <df[,1] [19,999 × 1]> +#> 3 <df[,7] [9 × 7]>
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html new file mode 100644 index 0000000..ff0dc62 --- /dev/null +++ b/docs/reference/curve_rev.html @@ -0,0 +1,261 @@ + + + + + + + + +Reverse engineer consonance and surprisal functions from confidence limits and point estimates — curve_rev • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Using the confidence limits and point estimates from a dataset, one can use these estimates to +compute thousands of consonance intervals and graph the intervals to form a consonance and +surprisal function.

    +
    + +
    curve_rev(point, LL, UL, measure = "default", steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    point

    The point estimate from an analysis. Ex: 1.20

    LL

    The lower confidence limit from an analysis Ex: 1.0

    UL

    The upper confidence limit from an analysis Ex: 1.4

    measure

    The type of data being used. If they involve mean differences, +then the "default" option should be used, which is also the default setting. +If the data are ratios, then the "ratio" option should be used.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more +consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended +as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    +# From a real published study. Point estimate of the result was hazard ratio of 1.61 and +# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. + +df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") + +tibble::tibble(df)
    #> # A tibble: 3 x 1 +#> df +#> <named list> +#> 1 <df[,7] [9,999 × 7]> +#> 2 <df[,1] [19,997 × 1]> +#> 3 <df[,7] [9 × 7]>
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html new file mode 100644 index 0000000..940f91e --- /dev/null +++ b/docs/reference/curve_surv.html @@ -0,0 +1,236 @@ + + + + + + + + +Produce Consonance Intervals for Survival Data — curve_surv • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the +Cox model computed by the 'survival' package and places the interval limits for each interval level +into a data frame along with the corresponding p-value and s-value.

    +
    + +
    curve_surv(data, x, steps = 10000)
    + +

    Arguments

    + + + + + + + + + + + + + + +
    data

    Object where the Cox model is stored, typically a list produced by the 'survival' package.

    x

    Predictor of interest within the survival model for which the consonance intervals should be computed.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting +this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more +consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended +as it will take longer to produce all the intervals and store them into a dataframe.

    + +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html new file mode 100644 index 0000000..be88c19 --- /dev/null +++ b/docs/reference/curve_table.html @@ -0,0 +1,269 @@ + + + + + + + + +Produces a Table of Relevant Statistics from the `concurve` functions +<!-- %% ~~function to do ... ~~ --> — curve_table • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    curve_table(data, levels, type = "data.frame")
    + +

    Arguments

    + + + + + + + + + + + + + + +
    data

    levels

    type

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + +(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Error in curve_table(intervalsdf, type = "data.frame"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "tibble"))
    #> Error in curve_table(intervalsdf, type = "tibble"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "latex"))
    #> Error in curve_table(intervalsdf, type = "latex"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "image"))
    #> Error in curve_table(intervalsdf, type = "image"): object 'subdf' not found
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html new file mode 100644 index 0000000..6e866dd --- /dev/null +++ b/docs/reference/defunct.html @@ -0,0 +1,268 @@ + + + + + + + + +Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +concurve. — defunct" /> + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    The functions listed below are deprecated. Please use the listed alternatives.

    +
    + +
    plotpint(...)
    +
    +plotsint(...)
    +
    +meanintervals(...)
    +
    +metaintervals(...)
    +
    +genintervals(...)
    +
    +corrintervals(...)
    +
    +survintervals(...)
    +
    +rev_eng(...)
    + + +

    plotpint

    + + + +

    For plotpint, use ggconcurve or plot_concurve.

    +

    plotsint

    + + + +

    For plotsint, use ggconcurve or plot_concurve.

    +

    meanintervals

    + + + +

    For meanintervals, use curve_mean.

    +

    metaintervals

    + + + +

    For metaintervals, use curve_meta.

    +

    genintervals

    + + + +

    For genintervals, use curve_gen.

    +

    corrintervals

    + + + +

    For corrintervals, use curve_corr.

    +

    survintervals

    + + + +

    For survintervals, use curve_surv.

    +

    rev_eng

    + + + +

    For rev_eng, use curve_rev.

    + +
    + +
    + + + +
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    +
    + + + + +
    + +
    +
    + + +
    +

    Takes the dataframe produced by the interval functions and plots the p-values/s-values, consonance (confidence) +levels, and the interval estimates to produce a p-value/s-value function using ggplot2 graphics.

    +
    + +
    ggconcurve(type, data, measure, nullvalue, position,
    +           title, subtitle, xaxis, yaxis, color, fill)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example ggconcurve(type = "surprisal") or ggconcurve(type = "consonance").

    data

    The dataframe produced by one of the interval functions in which the intervals are stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    nullvalue

    Indicates whether the null value for the measure should be plotted. By default, it is set to "absent", meaning it will not be plotted as a vertical line. Changing this to "present", will plot a vertical line at 0 when the measure is set to "default" and a vertical line at 1 when the measure is set to "ratio". For example, ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function By default, it is set to "pyramid", meaning the p-value function will stand right side up, like a pyramid. However, it can also be inverted via the option "inverted". This will also change the sequence of the y-axes to match the orientation. This can be set as such, ggconcurve(type = "consonance", data = df, position = "inverted")

    title

    A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333").

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, fill = "#333333").

    + +

    Value

    + +

    Plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels.

    +

    References

    + +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    # Simulate random data + +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf))
    #> NULL
    +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    #> NULL
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> NULL
    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> NULL
    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level."))
    #> NULL
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..4a172e5 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,289 @@ + + + + + + + + +Function reference • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    curve_compare()

    +

    Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets

    +

    curve_corr()

    +

    Computes consonance intervals for correlations

    +

    curve_gen()

    +

    Computes consonance intervals for linear models

    +

    curve_lik()

    +

    Computes Likelihood Intervals for Ratio Data +

    +

    curve_mean()

    +

    Computes consonance intervals for mean differences

    +

    curve_meta()

    +

    Computes consonance intervals for meta-analysis data

    +

    curve_rev()

    +

    Reverse engineer consonance and surprisal functions from confidence limits and point estimates

    +

    curve_surv()

    +

    Produce Consonance Intervals for Survival Data

    +

    curve_table()

    +

    Produces a Table of Relevant Statistics from the `concurve` functions +

    +

    ggconcurve()

    +

    Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2

    +

    plot_compare()

    +

    Compares Different Functions on the Same Plot +

    +

    plot_concurve()

    +

    Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics.

    +
    + + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/plot_compare.html b/docs/reference/plot_compare.html new file mode 100644 index 0000000..53c45b4 --- /dev/null +++ b/docs/reference/plot_compare.html @@ -0,0 +1,329 @@ + + + + + + + + +Compares Different Functions on the Same Plot +<!-- %% ~~function to do ... ~~ --> — plot_compare • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    plot_compare(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", xaxis = "Θ Range of Values", yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b")
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    data1

    data2

    type

    measure

    nullvalue

    position

    title

    subtitle

    xaxis

    yaxis

    color

    fill1

    fill2

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values")))
    #> NULL
    +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    #> NULL
    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> NULL
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    #> NULL
    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    #> NULL
    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> NULL
    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level."))
    #> NULL
    + +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> NULL
    +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> NULL
    + +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/plot_concurve.html b/docs/reference/plot_concurve.html new file mode 100644 index 0000000..4b2ff34 --- /dev/null +++ b/docs/reference/plot_concurve.html @@ -0,0 +1,285 @@ + + + + + + + + +Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics. — plot_concurve • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Takes the dataframe produced by the interval functions and plots the p-values, s-values, consonance (confidence) +levels, and the interval estimates to produce p- and s-value functions using base R graphics.

    +
    + +
    plot_concurve(type, data, measure, intervals, title, xlab, ylab1, ylab2, fontsize, fill)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example plot_concurve(type = "surprisal") or plot_concurve(type = "consonance").

    data

    Dataframe where the results from a curve_ function is stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    intervals

    Indicates whether the limits for different consonance levels should be plotted on the graph. By default, this is set to FALSE. When set to TRUE, it will plot 50%, 75%, 95%, and 99% intervals.

    title

    The title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, title = "Custom Title").

    xlab

    The label for the x-axis. By default, it is set to "Theta.". In order to set a label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, xlab = "Custom Caption").

    ylab1

    A label for the y-axis on the left side of the graph. By default, it is set to "P-value." In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab1= "Custom y-axis title").

    ylab2

    A label for the y-axis on the right side of the graph. By default, it is set to "Confidence Level."" In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab2= "Custom y-axis title").

    fontsize

    Controls the font size in the graphs. By default, it is set to size 12.).

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes.

    + +

    Value

    + +

    Plot with intervals at every consonance level graphed with their corresponding p- and s-values.

    +

    References

    + +

    Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: +There Is No Replication Crisis If We Don’t Expect Replication. Am Stat; 2018.

    +

    Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values +and their resolution with S-values. Am Stat. 2018;18(136).

    +

    Greenland S. The unconditional information in P-values, and its refutational interpretation +via S-values. 2018.

    +

    Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. +1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x

    +

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    +

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    +

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    + +

    Examples

    +
    # Simulate random data + +GroupA <- rnorm(50) +GroupB <- rnorm(50) + +RandomData <- data.frame(GroupA, GroupB) +RandomModel <- lm(GroupA ~ GroupB, data = RandomData) + +intervalsdf <- curve_gen(RandomModel, "GroupB") + +plot_concurve(type = "consonance", data = intervalsdf)
    #> Error in plot(lower.limit, pvalue, xlim = c(min(lower.limit), max(upper.limit)), panel.first = grid(ny = 0), type = "l", xlab = xlab, ylab = ylab1, main = title, yaxt = "n", las = 1): object 'lower.limit' not found
    +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..ce2552b --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,51 @@ + + + + https://data.lesslikely.com/concurve//index.html + + + https://data.lesslikely.com/concurve//reference/curve_compare.html + + + https://data.lesslikely.com/concurve//reference/curve_corr.html + + + https://data.lesslikely.com/concurve//reference/curve_gen.html + + + https://data.lesslikely.com/concurve//reference/curve_lik.html + + + https://data.lesslikely.com/concurve//reference/curve_mean.html + + + https://data.lesslikely.com/concurve//reference/curve_meta.html + + + https://data.lesslikely.com/concurve//reference/curve_rev.html + + + https://data.lesslikely.com/concurve//reference/curve_surv.html + + + https://data.lesslikely.com/concurve//reference/curve_table.html + + + https://data.lesslikely.com/concurve//reference/defunct.html + + + https://data.lesslikely.com/concurve//reference/ggconcurve.html + + + https://data.lesslikely.com/concurve//reference/plot_compare.html + + + https://data.lesslikely.com/concurve//reference/plot_concurve.html + + + https://data.lesslikely.com/concurve//articles/examples.html + + + https://data.lesslikely.com/concurve//articles/stata.html + + diff --git a/examples/Untitled.R b/examples/Untitled.R new file mode 100644 index 0000000..f79a753 --- /dev/null +++ b/examples/Untitled.R @@ -0,0 +1,209 @@ + +library(concurve) +library(rstan) +library(rstanarm) + +GroupA2<-rnorm(1000) +GroupB2<-rnorm(1000) + +RandomData2<-data.frame(GroupA2, GroupB2) + +model<-lm(GroupA2 ~ GroupB2, data=RandomData2) +model2 <- stan_glm(GroupA2 ~ GroupB2, data = RandomData2) + +z<- bayes_R2(model2) +install.packages("tidybayes") + +median(z) + +summary(model) +summary(model2) +model2 +library(tidybayes) +install.packages("broom") +library(broom) +tidymodel<- tidy(model2) + +model_intercept <- tidymodel$estimate[1] +model_slope <- tidymodel$estimate[2] +draws <- spread_draws(model2, `(Intercept)`, GroupB2) + +library(ggplot2) +ggplot() + + geom_point(data = RandomData2, mapping = aes(x = GroupB2, y = GroupA2)) + + geom_abline(data = draws, aes(intercept = `(Intercept)`, slope = GroupB2), color = "skyblue", alpha = 0.2, size = 0.2) + + geom_abline(slope = model_slope, intercept = model_intercept) + + +ggplot(data = RandomData2, mapping = aes(x = GroupB2, y = GroupA2)) + + geom_point() + + geom_smooth(se = TRUE) + +posterior_interval(model2) + +system.time(randomframe<-genintervals(model, "GroupB2")) + + + +par(mar = c(5, 5, 4, 5), font.main = 1, cex.main = 1.3, cex.lab = 1.15, las = 1) +with(randomframe, + plot(lower.limit, pvalue, + xlim = c(min(lower.limit), max(upper.limit)), + panel.first = grid(nx = 10, ny = 0), + type = "l", + xlab = "Theta", + ylab = "P-value", + main = "P-value Function", + yaxt = "n", + las = 1)) +with(randomframe, lines(upper.limit, pvalue)) +polygon(c(max(randomframe$lower.limit), randomframe$lower.limit), + c(min(randomframe$pvalue), randomframe$pvalue), col = "#239a9880", border = NA) +polygon(c(min(randomframe$upper.limit), randomframe$upper.limit), + c(min(randomframe$pvalue), randomframe$pvalue), col = "#239a9880", border = NA) +axis(side = 2, at = c(seq(from = 0, to = 1, by = .10)), lty = 2, col = "grey", las = 1) +abline(v = 0, lty = 2, lwd = 1.2, col = "black") +par(new = T) +axis(side = 2, at = c(seq(from = 0, to = 1, by = .10)), + tck = -0.025, lty = 2, col = "grey", labels = NA) +par(new = T) +with(randomframe, + plot(1, type = "n", + ylim = rev(range(intrvl.level * 100)), + axes = F, + xlab = NA, + ylab = NA)) +axis(side = 4, at = c(seq(from = 0, to = 100, by = 10)), + tck = 1, lty = 2, col = "grey", las = 1) +text(par("usr") + 0.95, 28, srt=-90, adj = 0, labels = "Consonance Level (%)", cex = 1.05, + xpd = TRUE) +par(new = T) +axis(side = 4, at = c(seq(from = 0, to = 100, by = 10)), + tck = -0.025, lty = 2, col = "grey", labels = NA) +text(x = 1.25, y = 10, paste("MLE:", + round(((max(randomframe$lower.limit) + min(randomframe$upper.limit)) / 2), 3)), + cex = 0.8, col = "black") +text(x = 1.25, y = 15, paste("50% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.50))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.50))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 20, paste("75% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.25))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.75))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 25, paste("95% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.05))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.95))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 30, paste("99% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.01))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.99))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") + + + + + +par(mar = c(5, 5, 4, 5), font.main = 1, cex.main = 1.3, cex.lab = 1.15, las = 1) +with(randomframe, + plot(lower.limit, svalue, + xlim = c(min(lower.limit), max(upper.limit)), + panel.first = grid(), + type = "l", + xlab = "Theta", + ylab = "S-value", + main = "Surpisal Function", + yaxt = "n", + las = 1)) +with(randomframe, lines(upper.limit, svalue)) +polygon(c(max(randomframe$lower.limit), randomframe$lower.limit), + c(max(randomframe$svalue), randomframe$svalue), col = "#239a9880", border = NA) +polygon(c(min(randomframe$upper.limit), randomframe$upper.limit), + c(max(randomframe$svalue), randomframe$svalue), col = "#239a9880", border = NA) +axis(side = 2, lty = 2, col = "grey", las = 1) +par(new = T) +axis(side = 2, tck = -0.025, lty = 2, col = "grey", labels = NA) +par(new = T) +with(randomframe, + plot(1, type = "n", + ylim = rev(range(intrvl.level * 100)), + axes = F, + xlab = NA, + ylab = NA)) +axis(side = 4, at = c(seq(from = 0, to = 100, by = 10)), + tck = 1, lty = 2, col = "grey", las = 1) +text(par("usr") + 0.95, 28, srt=-90, adj = 0, labels = "Consonance Level (%)", cex = 1.05, + xpd = TRUE) +par(new = T) +axis(side = 4, at = c(seq(from = 0, to = 100, by = 10)), + tck = -0.025, lty = 2, col = "grey", labels = NA) +text(x = 1.25, y = 10, paste("MLE:", + round(((max(randomframe$lower.limit) + min(randomframe$upper.limit)) / 2), 3)), + cex = 0.8, col = "black") +text(x = 1.25, y = 15, paste("50% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.50))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.50))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 20, paste("75% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.25))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.75))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 25, paste("95% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.05))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.95))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") +text(x = 1.25, y = 30, paste("99% CI:", + round(unname((quantile(randomframe$lower.limit, prob = 0.01))), 3), "-", + round(unname((quantile(randomframe$upper.limit, prob = 0.99))), 3), sep=" ", collapse=", "), + cex = 0.8, col = "black") + + + + + +ggconcurve("consonance", randomframe, position = "pyramid", nullvalue = "present") +plot.concurve(type = "consonance", data = randomframe) + +library(microbenchmark) +library(ggplot2) +library(concurve) + +microbenchmark( + base_R = plot_concurve(randomframe), + ggplot_R = gg_consonance(randomframe) +) + +library(benchplot) + +benchplot(gg_consonance(randomframe)) + + +install.packages("benchplot") + +gg_surprisal(randomframe) + + + + + + +library(ProfileLikelihood) + +profilelike.lm + +dffer<- curve_lik(formula = GroupA2 ~ 1, data = RandomData2, + profile.theta= "GroupB2", lo.theta = -.3, hi.theta = .3, length = 500) + +(plotlikely <- gglikely(dffer, x = dffer$theta, y = dffer$log.norm.lik)) + +library(benchmarkme) +ram <- get_ram() +ram +cpu <- get_cpu() +cpu + +# Run the io benchmark +res <- benchmark_io(runs = 1, size = 5) + +# Plot the results +plot(res) diff --git a/examples/boot.R b/examples/boot.R new file mode 100644 index 0000000..defa202 --- /dev/null +++ b/examples/boot.R @@ -0,0 +1,73 @@ +library(boot) +library(boot) +library(Lock5Data) +dataz <- data(CommuteAtlanta) + +func = function(data, index) { + x <- as.numeric(unlist(data[1])) + y <- as.numeric(unlist(data[2])) + return(mean(x[index]) - mean(y[index])) +} + +library(parallel) +detectCores() + +intervals <- 1:1000/1000 + +set.seed(1031) + +GroupA <- runif(100, min = 0, max = 100) +GroupB <- runif(100, min = 0, max = 100) + +RandomData <- data.frame(GroupA, GroupB) + +replicates <- 20000 + + +t.boot = boot(data = CommuteAtlanta, func, replicates, parallel = "multicore", ncpus = (detectCores())) + +intrvls <- 1:1000/1000 + +library(parallel) +library(pbapply) +library(microbenchmark) +library(boot) +library(concurve) + +microbenchmark( +t = mclapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], + mc.cores = detectCores()), +a = pblapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], + cl = detectCores())) + +df <- data.frame(do.call(rbind, t)) +intrvl.limit <- c("lower.limit", "upper.limit") +colnames(df) <- intrvl.limit +df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit))) +df$intrvl.level <- intrvls +df$cdf <- (abs(df$intrvl.level / 2)) + 0.5 +df$pvalue <- 1 - intrvls +df$svalue <- -log2(df$pvalue) +df <- head(df, -1) +class(df) <- c("data.frame","concurve") + + +densdf <- data.frame(c(df$lower.limit, df$upper.limit)) +colnames(densdf) <- "x" +densdf <- head(densdf, -1) +class(densdf) <- c("data.frame","concurve") +plot(density(densdf$x)) + +if (table == TRUE) { + levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) + (df_subintervals <- (curve_table(df, levels, type = "data.frame"))) + class(df_subintervals) <- c("data.frame","concurve") + dataframes <- list(df, densdf, df_subintervals) + names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table") + class(dataframes) <- "concurve" + return(dataframes) + +} else if (table == FALSE) { + return(list(df, densdf)) +} + diff --git a/examples/examples.R b/examples/examples.R new file mode 100644 index 0000000..df595a3 --- /dev/null +++ b/examples/examples.R @@ -0,0 +1,233 @@ +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + +(intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +)) + + +(z <- curve_table(intervalsdf[[1]], format = "data.frame")) +(z <- curve_table(intervalsdf[[1]], format = "tibble")) +# (z <- curve_table(intervalsdf, format = "docx")) +# (z <- curve_table(intervalsdf, format = "pptx")) +(z <- curve_table(intervalsdf[[1]], format = "latex")) +(z <- curve_table(intervalsdf[[1]], format = "image")) + +(function1 <- ggconcurve(data = intervalsdf[[1]], type = "c")) +(function1 <- ggconcurve(data = intervalsdf[[1]], type = "s")) +(function1s <- ggconcurve(data = intervalsdf[[2]], type = "cdf")) + + +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) + +(curve_compare( + data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", + plot = TRUE, measure = "default", nullvalue = TRUE +)) + +(curve_compare( + data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", + plot = TRUE, measure = "default", nullvalue = FALSE +)) + +(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) + + +(df1 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)) ## may take some time +(df2 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)) ## may take some time + + +(r <- (curve_compare( + data1 = df1[[1]], data2 = df2[[1]], type = "c", + plot = TRUE, measure = "ratio", nullvalue = TRUE +))) + +(curve_compare( + data1 = df1[[1]], data2 = df2[[1]], type = "s", + plot = TRUE, measure = "ratio", nullvalue = FALSE +)) + +(plot_compare( + data1 = df1[[1]], data2 = df2[[1]], type = "c", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. Brown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 95% CL: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 95% CL: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) +(plot_compare( + data1 = df1[[1]], data2 = df2[[1]], type = "s", measure = "ratio", nullvalue = FALSE, title = "Brown et al. 2017. J Clin Psychiatry. vs. Brown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 95% CL: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 95% CL: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) + + +lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000) +(ggconcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) + + + +lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000) +(ggconcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) + + +(plot_compare( + data1 = lik1[[1]], data2 = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) +(plot_compare( + data1 = lik1[[1]], data2 = lik2[[1]], type = "d", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) + + + +(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", measure = "default", nullvalue = TRUE)) + +plot_compare(data1 = lik1[[1]], data2 = lik2[[1]], type = "d", measure = "ratio", nullvalue = FALSE) + +(function4 <- ggconcurve(randomframe[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +) + + +# Bootstrapping ----------------------------------------------------------- + +data(diabetes, package = "bcaboot") +Xy <- cbind(diabetes$x, diabetes$y) +rfun <- function(Xy) { + y <- Xy[, 11] + X <- Xy[, 1:10] + return(summary(lm(y ~ X))$adj.r.squared) +} + +(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000)) + +ggconcurve(data = x[[1]]) +ggconcurve(data = x[[3]]) + +plot_compare(x[[1]], x[[3]]) + + +iris <- datasets::iris +foo <- function(data, indices) { + dt <- data[indices, ] + c( + cor(dt[, 1], dt[, 2], method = "p") + ) +} + +y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000) + + +ggconcurve(data = y[[1]]) +ggconcurve(data = y[[3]]) + +plot_compare(y[[1]], y[[3]]) + + + +library(Lock5Data) +dataz <- data(CommuteAtlanta) + +func = function(data, index) { + x <- as.numeric(unlist(data[1])) + y <- as.numeric(unlist(data[2])) + return(mean(x[index]) - mean(y[index])) +} + +z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 200, steps = 1000) +ggconcurve(data = z[[1]]) +ggconcurve(data = z[[2]], type = "cd") + +# Meta-Analysis ----------------------------------------------------------- + +GroupAData <- runif(20, min = 0, max = 100) +GroupAMean <- round(mean(GroupAData), digits = 2) +GroupASD <- round(sd(GroupAData), digits = 2) + +GroupBData <- runif(20, min = 0, max = 100) +GroupBMean <- round(mean(GroupBData), digits = 2) +GroupBSD <- round(sd(GroupBData), digits = 2) + +GroupCData <- runif(20, min = 0, max = 100) +GroupCMean <- round(mean(GroupCData), digits = 2) +GroupCSD <- round(sd(GroupCData), digits = 2) + +GroupDData <- runif(20, min = 0, max = 100) +GroupDMean <- round(mean(GroupDData), digits = 2) +GroupDSD <- round(sd(GroupDData), digits = 2) + + +StudyName <- c("Study1", "Study2") +MeanTreatment <- c(GroupAMean, GroupCMean) +MeanControl <- c(GroupBMean, GroupDMean) +SDTreatment <- c(GroupASD, GroupCSD) +SDControl <- c(GroupBSD, GroupDSD) +NTreatment <- c(20, 20) +NControl <- c(20, 20) + +metadf <- data.frame( + StudyName, MeanTreatment, MeanControl, + SDTreatment, SDControl, + NTreatment, NControl +) + +library(metafor) + +dat <- escalc( + measure = "SMD", + m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, + m2i = MeanControl, sd2i = SDControl, n2i = NControl, + data = metadf +) + + +res <- rma(yi, vi, + data = dat, slab = paste(StudyName, sep = ", "), + method = "FE", digits = 2 +) + +res + + +metaf <- curve_meta(res) +tibble::tibble(metaf[[1]]) + +res2 <- rma(yi, vi, + data = dat, slab = paste(StudyName, sep = ", "), + method = "REML", digits = 2 +) + +res2 + + +metaf2 <- curve_meta(res2) +tibble::tibble(metaf2[[1]]) + + +(function5 <- ggconcurve(metaf[[1]], type = "c")) + + +(function6 <- ggconcurve(metaf[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +) + +(function7 <- ggconcurve(metaf2[[1]], type = "c")) + + +(function8 <- ggconcurve(metaf2[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +) + +(curve_compare(data1 = metaf[[1]], data2 = metaf2[[1]], type = "c", plot = TRUE, measure = "default") +) +(curve_compare(data1 = metaf[[1]], data2 = metaf2[[1]], type = "s", plot = TRUE, measure = "default") +) + diff --git a/inst/CITATION b/inst/CITATION index b895d4a..949e0ff 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,11 +1,11 @@ -year <- sub("-.*", "", meta$Date) -note <- sprintf("R package version %s", meta$Version) +citHeader("To cite concurve in publications use:") -bibentry( - bibtype = "Manual", - title = paste("{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions"), - author = c(person("Zad R.", "Chow"), person("Andrew D.", "Vigotsky")), - year = year, - note = note, - url = "https://CRAN.R-project.org/package=concurve" +citEntry( + entry = "Manual", + title = paste("{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions"), + author = c(person("Zad R.", "Chow"), person("Andrew D.", "Vigotsky")), + year = 2019, + url = "https://CRAN.R-project.org/package=concurve", + textVersion = paste( + ) ) diff --git a/inst/WORDLIST b/inst/WORDLIST new file mode 100644 index 0000000..bf5e028 --- /dev/null +++ b/inst/WORDLIST @@ -0,0 +1,71 @@ +AJPH +Amrhein +ANCOVA +arXiv +DAS +databrowser +df +dichotomization +doi +DOI +dplyr +estiate +flextable +forvalues +functons +ggconcurve +ggplot +glm +GroupB +Hjort +https +intervalsdf +jstatsoft +kendall +KJ +knitr +lm +logarithmically +lointerval +mathST +metafor +NL +nullvalue +paranthesis +pearson +postfile +Powerpoint +ProfileLikelihood +pvalue +Pvalue +Rdoc +README +refutational +relace +Rothman +Saltelli +Scand +Schweder +Softw +spearman +Stata +Strawderman +surprisal +Surprisal +survminer +svalue +Svalue +tibble +tidyr +topost +Trafimow +Twoway +upinterval +Viechtbauer +wald +www +xaxis +Xie +xlab +ylab +yxis diff --git a/revdep/.gitignore b/revdep/.gitignore new file mode 100644 index 0000000..31f6c40 --- /dev/null +++ b/revdep/.gitignore @@ -0,0 +1,6 @@ +checks +library +checks.noindex +library.noindex +data.sqlite +*.html diff --git a/revdep/email.yml b/revdep/email.yml new file mode 100644 index 0000000..0c5cef8 --- /dev/null +++ b/revdep/email.yml @@ -0,0 +1,5 @@ +release_date: ??? +rel_release_date: ??? +my_news_url: ??? +release_version: ??? +release_details: ??? diff --git a/tests/spelling.R b/tests/spelling.R new file mode 100644 index 0000000..13f77d9 --- /dev/null +++ b/tests/spelling.R @@ -0,0 +1,6 @@ +if (requireNamespace("spelling", quietly = TRUE)) { + spelling::spell_check_test( + vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE + ) +} From c082f7263608df0795456d9958c706808899adc1 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Mon, 2 Dec 2019 23:41:16 -0500 Subject: [PATCH 12/21] usethis --- .Rbuildignore | 2 +- R/concurve-package.R | 8 ++++++++ cran-comments.md | 8 +++----- inst/WORDLIST | 3 ++- tests/spelling.R | 9 +++------ dependencies.R => usethis.R | 11 ++++++++++- 6 files changed, 27 insertions(+), 14 deletions(-) create mode 100644 R/concurve-package.R rename dependencies.R => usethis.R (81%) diff --git a/.Rbuildignore b/.Rbuildignore index 4d2b026..cbbb201 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -22,6 +22,6 @@ ^\.circleci$ ^revdep$ ^Makefile$ -^dependencies\.R$ ^\.covrignore$ ^LICENSE\.md$ +^usethis\.R$ diff --git a/R/concurve-package.R b/R/concurve-package.R new file mode 100644 index 0000000..b30bbda --- /dev/null +++ b/R/concurve-package.R @@ -0,0 +1,8 @@ +#' @keywords internal +"_PACKAGE" + +# The following block is used by usethis to automatically manage +# roxygen namespace tags. Modify with care! +## usethis namespace: start +## usethis namespace: end +NULL diff --git a/cran-comments.md b/cran-comments.md index 2ddff8f..9a5cfc3 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,9 +1,7 @@ ## Test environments - -* local: darwin15.6.0-3.6.1 -* travis: 3.1, 3.2, 3.3, oldrel, release, devel -* r-hub: windows-x86_64-devel, ubuntu-gcc-release, fedora-clang-devel -* win-builder: windows-x86_64-devel +* local OS X install, R 3.6.1 +* ubuntu 14.04 (on travis-ci), R 3.6.1 +* win-builder (devel and release) ## R CMD check results diff --git a/inst/WORDLIST b/inst/WORDLIST index bf5e028..4b8af58 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -2,12 +2,12 @@ AJPH Amrhein ANCOVA arXiv +Codecov DAS databrowser df dichotomization doi -DOI dplyr estiate flextable @@ -24,6 +24,7 @@ jstatsoft kendall KJ knitr +Lifecycle lm logarithmically lointerval diff --git a/tests/spelling.R b/tests/spelling.R index 13f77d9..6713838 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,6 +1,3 @@ -if (requireNamespace("spelling", quietly = TRUE)) { - spelling::spell_check_test( - vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE - ) -} +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE) diff --git a/dependencies.R b/usethis.R similarity index 81% rename from dependencies.R rename to usethis.R index 389c6bc..d951099 100644 --- a/dependencies.R +++ b/usethis.R @@ -1,3 +1,5 @@ +# Importing other packages + use_package("parallel", "Imports", min_version = NULL) use_package("pbmcapply", "Imports", min_version = NULL) use_package("compiler", "Imports", min_version = NULL) @@ -16,7 +18,14 @@ use_package("survival", "Imports", min_version = NULL) use_package("survminer", "Imports", min_version = NULL) use_package("scales", "Imports", min_version = NULL) - +# Suggest other packages use_package("testthat", "Suggests", min_version = NULL) use_package("covr", "Suggests", min_version = NULL) use_package("spelling", "Suggests", min_version = NULL) + +# Other helper functions + +use_build_ignore("usethis.R", escape = TRUE) +use_spell_check(vignettes = TRUE, lang = "en-US", error = FALSE) +use_cran_comments(open = interactive()) +use_tidy_style() From 8fcfe0ec2ed4b965f52e5fc5b5e7a6de6d2d1f6e Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 3 Dec 2019 02:17:55 -0500 Subject: [PATCH 13/21] Roxygen comments added directly to .R files --- R/curve_boot.R | 37 +++++++- R/curve_compare.R | 27 ++++++ R/curve_corr.R | 29 +++++++ R/curve_gen.R | 34 +++++++- R/curve_lik.R | 12 +++ R/curve_mean.R | 36 +++++++- R/curve_meta.R | 68 ++++++++++++++- R/curve_rev.R | 33 ++++++- R/curve_surv.R | 22 ++++- R/curve_table.R | 32 +++++++ R/ggconcurve.R | 66 ++++++++++++++ R/plot_compare.R | 64 ++++++++++++++ inst/WORDLIST | 29 ++++--- man/concurve-package.Rd | 48 +++++++++++ man/curve_boot.Rd | 56 ++++++++++++ man/curve_compare.Rd | 82 +++++++----------- man/curve_corr.Rd | 68 ++++++--------- man/curve_gen.Rd | 72 +++++++--------- man/curve_lik.Rd | 78 +++-------------- man/curve_mean.Rd | 89 +++++++++---------- man/curve_meta.Rd | 83 +++++++----------- man/curve_rev.Rd | 75 ++++++++-------- man/curve_surv.Rd | 48 +++++------ man/curve_table.Rd | 79 ++++++----------- man/ggconcurve.Rd | 165 +++++++++++++++++------------------ man/pipe.Rd | 12 +++ man/plot_compare.Rd | 185 +++++++++++++++++----------------------- man/plot_concurve.Rd | 87 ------------------- man/tidyeval.Rd | 51 +++++++++++ 29 files changed, 1047 insertions(+), 720 deletions(-) create mode 100644 man/concurve-package.Rd create mode 100644 man/curve_boot.Rd create mode 100644 man/pipe.Rd delete mode 100644 man/plot_concurve.Rd create mode 100644 man/tidyeval.Rd diff --git a/R/curve_boot.R b/R/curve_boot.R index b5ebb28..591e8c5 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -1,8 +1,41 @@ +#' Generate Consonance Functions via Bootstrapping +#' +#' Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages +#' to generate consonance distrbutions. +#' +#' @param data Dataset that is being used to create a consonance function +#' @param func Custom function that is used to create parameters of interest that +#' will be bootstrapped +#' @param method The boostrap method that will be used to generate the functions. +#' Methods include "bca" which is the default and "t". +#' +#' @param replicates Indicates how many bootstrap replicates are to be performed. The default +#' is currently 20000 but more may be desirable, especially to make the functions more smooth +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce +#' all the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should +#' be generated. The default is TRUE and generates a table which is included in the list object +#' +#' @return A list with the dataframe of values in the first list and the table in the second +#' if table = TRUE +#' +#' @examples +#' iris <- datasets::iris +#' foo <- function(data, indices) { +#' foo <- function(data, indices) { +#' dt <- data[indices, ] +#' c( +#' cor(dt[, 1], dt[, 2], method = "p") +#' ) +#' } +#' (y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000)) +#' } curve_boot <- function(data = data, func = func, method = "bca", replicates = 20000, steps = 1000, table = TRUE) { - - # BCA Bootstrap Method --------------------------------------------------- if (method == "bca") { diff --git a/R/curve_compare.R b/R/curve_compare.R index edab721..2ec08df 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -1,3 +1,30 @@ +#' Compares two functions and produces an AUC score to show the amount of consonance. +#' +#' Compares the p-value/s-value, and likelihood functions and computes an AUC number. +#' +#' @param data1 The first dataframe produced by one of the interval functions in which the intervals are stored. +#' @param data2 The second dataframe produced by one of the interval functions in which the intervals are stored. +#' @param type Choose whether to plot a "consonance" function, a "surprisal" function or +#' "likelihood". The default option is set to "c". The type must be set in quotes, for example +#' ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance +#' distribution function, and "cd" for the consonance density function, "l1" for relative +#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. +#' @param plot by default it is set to TRUE and will use the plot_compare() function to plot the two functions +#' @param ... Can be used to pass further arguments to plot_compare() +#' @examples +#' library(concurve) +#' GroupA <- rnorm(500) +#' GroupB <- rnorm(500) +#' RandomData <- data.frame(GroupA, GroupB) +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +#' GroupA2 <- rnorm(500) +#' GroupB2 <- rnorm(500) +#' RandomData2 <- data.frame(GroupA2, GroupB2) +#' model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +#' randomframe <- curve_gen(model, "GroupB2") +#' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) +#' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) +#' # curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { # Consonance Function ----------------------------------------------------- diff --git a/R/curve_corr.R b/R/curve_corr.R index 6e1bd78..a27f716 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -1,3 +1,32 @@ +#' Computes Consonance Intervals for Correlations +#' +#' Computes consonance intervals to produce P- and S-value functions for correlational analyses +#' using the cor.test function in base R and places the interval limits for each interval level +#' into a data frame along with the corresponding p-values and s-values. +#' +#' @param x A vector that contains the data for one of the variables that will be analyzed for correlational analysis. +#' @param y A vector that contains the data for one of the variables that will be analyzed for correlational analysis. +#' @param alternative Indicates the alternative hypothesis and must be one of "two.sided", +#' "greater" or "less". You can specify just the initial letter. "greater" corresponds to +#' positive association, "less" to negative association. +#' @param method A character string indicating which correlation coefficient is to be used for +#' the test. One of "pearson", "kendall", or "spearman", can be abbreviated. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce all +#' the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should be +#' generated. The default is TRUE and generates a table which is included in the list object. +#' +#' @return +#' @export +#' +#' @examples +#' GroupA <- rnorm(50) +#' GroupB <- rnorm(50) +#' joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") +#' tibble::tibble(joe) curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { if (is.numeric(x) != TRUE) { stop("Error: 'x' must be a numeric vector") diff --git a/R/curve_gen.R b/R/curve_gen.R index 93f8fac..01eba82 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -1,5 +1,37 @@ # General Consonance Functions Using Profile Likelihood, Wald, or the bootstrap method for linear models. - +#' +#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the +# selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits +# for each interval level into a data frame along with the corresponding p-values and s-values. +#' +#' @param model The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here. +#' @param var The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced. +#' @param method Chooses the method to be used to calculate the consonance intervals. There are currently four +# methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to +# compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically +# what most people are familiar with when computing intervals based on the calculated standard error. +# The "lm" method allows this function to be used for specific scenarios like logistic regression and +# the 'glm' function. The "boot" method allows for bootstrapping at certain levels. +#' @param replicates Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 compatibility intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce all +#' the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should +# be generated. The default is TRUE and generates a table which is included in the list object +#' +#' @return +#' @export +#' +#' @examples +#' # Simulate random data +#' GroupA <- rnorm(50) +#' GroupB <- rnorm(50) +#' RandomData <- data.frame(GroupA, GroupB) +#' rob <- glm(GroupA ~ GroupB, data = RandomData) +#' bob <- curve_gen(rob, "GroupB", method = "lm") +#' tibble::tibble(bob) curve_gen <- function(model, var, method = "wald", replicates = 1000, steps = 10000, table = TRUE, ...) { if (is.character(method) != TRUE) { stop("Error: 'method' must be a character vector") diff --git a/R/curve_lik.R b/R/curve_lik.R index f6f55d7..fddc465 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -1,3 +1,15 @@ +#' Compute the Profile Likelihood Functions +#' +#' @param likobject An object from the ProfileLikelihood package +#' @param data The dataframe that was used to create the likelihood object in the ProfileLikelihood package. +#' @param table Indicates whether or not a table output with some relevant statistics should +# be generated. The default is TRUE and generates a table which is included in the list object +#' +#' @examples +#' +#' data(dataglm) +#' xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) +#' (curve_lik(xx, dataglm)) curve_lik <- function(likobject, data, table = TRUE) { values <- likobject[[1]] # theta values likelihood <- likobject[[2]] # profile likelihoods diff --git a/R/curve_mean.R b/R/curve_mean.R index a00fd1b..8380606 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -1,5 +1,37 @@ -# Mean Interval Consonance Function - +#' Mean Interval Consonance Function +#' +#' Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test +#' that compares means and places the interval limits for each interval level into a data +#' frame along with the corresponding p-values and s-values. +#' +#' @param x Variable that contains the data for the first group being compared. +#' @param y Variable that contains the data for the second group being compared. +#' @param data Data frame from which the variables are being extracted from. +#' @param paired Indicates whether the statistical test is a paired difference test. +#' By default, it is set to "F",which means the function will be an unpaired statistical test +#' comparing two independent groups.Inserting "paired" will change the test to a paired +#' difference test. +#' @param method By default this is turned off (set to "default"), but allows for bootstrapping +#' if "boot" is insertedinto the function call. +#' @param replicates Indicates how many bootstrap replicates are to be performed if bootstrapping +#' is enabled as a method. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, settingthis to 100 will produce 100 consonance intervals from 0 to 100. Setting +#' this to 10000 will produce moreconsonance levels. By default, it is set to 1000. Increasing +#' the number substantially is not recommended as it will take longer to produce all the intervals +#' and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should +#' be generated. The default is TRUE and generates a table which is included in the list +#' object. +#' +#' @examples +#' +#' # Simulate random data +#' GroupA <- runif(100, min = 0, max = 100) +#' GroupB <- runif(100, min = 0, max = 100) +#' RandomData <- data.frame(GroupA, GroupB) +#' bob <- curve_mean(GroupA, GroupB, RandomData) +#' tibble::tibble(bob) curve_mean <- function(x, y, data, paired = F, method = "default", replicates = 1000, steps = 10000, table = TRUE) { if (is.numeric(x) != TRUE) { stop("Error: 'x' must be a numeric vector") diff --git a/R/curve_meta.R b/R/curve_meta.R index 9bec2cb..3278783 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -1,5 +1,69 @@ -# Meta-analytic Consonance Function - +#' Meta-analytic Consonance Function +#' +#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the +#' meta-analysis done by the metafor package and places the interval limits for each interval level +#' into a data frame along with the corresponding p-values and s-values. +#' +#' @param x Object where the meta-analysis parameters are stored, typically a list produced by 'metafor' +#' @param measure Indicates whether the object has a log transformation or is normal/default. +#' The default setting is "default. If the measure is set to "ratio", it will take +#' logarithmically transformed values and convert them back to normal values in the dataframe. +#' This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce all +#' the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics +#' should be generated. The default is TRUE and generates a table which is included in the +#' list object. +#' +#' @examples +#' +#' # Simulate random data for two groups in two studies +#' GroupAData <- runif(20, min = 0, max = 100) +#' GroupAMean <- round(mean(GroupAData), digits = 2) +#' GroupASD <- round(sd(GroupAData), digits = 2) +#' +#' GroupBData <- runif(20, min = 0, max = 100) +#' GroupBMean <- round(mean(GroupBData), digits = 2) +#' GroupBSD <- round(sd(GroupBData), digits = 2) +#' +#' GroupCData <- runif(20, min = 0, max = 100) +#' GroupCMean <- round(mean(GroupCData), digits = 2) +#' GroupCSD <- round(sd(GroupCData), digits = 2) +#' +#' GroupDData <- runif(20, min = 0, max = 100) +#' GroupDMean <- round(mean(GroupDData), digits = 2) +#' GroupDSD <- round(sd(GroupDData), digits = 2) +#' +#' # Combine the data +#' +#' StudyName <- c("Study1", "Study2") +#' MeanTreatment <- c(GroupAMean, GroupCMean) +#' MeanControl <- c(GroupBMean, GroupDMean) +#' SDTreatment <- c(GroupASD, GroupCSD) +#' SDControl <- c(GroupBSD, GroupDSD) +#' NTreatment <- c(20, 20) +#' NControl <- c(20, 20) +#' +#' metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl) +#' +#' # Use metafor to calculate the standardized mean difference +#' +#' library(metafor) +#' # +#' dat <- escalc(measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf) +#' # +#' # Pool the data using a particular method. Here "FE" is the fixed-effects model +#' # +#' res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) +#' # +#' # Calculate the intervals using the metainterval function +#' # +#' metaf <- curve_meta(res) +#' # +#' tibble::tibble(metaf) curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { if (is.list(x) != TRUE) { stop("Error: 'x' must be a list from 'metafor'") diff --git a/R/curve_rev.R b/R/curve_rev.R index 3221740..c40a04d 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -1,5 +1,34 @@ -# Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits - +#' Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits +#' +#' Using the confidence limits and point estimates from a dataset, one can use these +#' estimates to compute thousands of consonance intervals and graph the intervals to +#' form a consonance and surprisal function. +#' +#' @param point The point estimate from an analysis. Ex: 1.20 +#' @param LL The lower confidence limit from an analysis Ex: 1.0 +#' @param UL The upper confidence limit from an analysis Ex: 1.4 +#' @param type Indicates whether the produced result should be a consonance function +#' or a likelihood function. The default is "c" for consonance and likelihood can be set via +#' "l". +#' @param measure The type of data being used. If they involve mean differences, +# then the "default" option should be used, which is also the default setting. +# If the data are ratios, then the "ratio" option should be used. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce +#' all the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should +#' be generated. The default is TRUE and generates a table which is included in the list object. +#' +#' @examples +#' +#' # From a real published study. Point estimate of the result was hazard ratio of 1.61 and +#' # lower bound of the interval is 0.997 while upper bound of the interval is 2.59. +#' # +#' df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") +#' +#' tibble::tibble(df) curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10000, table = TRUE) { diff --git a/R/curve_surv.R b/R/curve_surv.R index 42490d5..a694a9a 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -1,5 +1,23 @@ -# Survival Data Consonance Function - +#' Produce Consonance Intervals for Survival Data +#' +#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the +#' Cox model computed by the 'survival' package and places the interval limits for each interval level +#' into a data frame along with the corresponding p-value and s-value. +#' +#' @param data Object where the Cox model is stored, typically a list produced by the +#' 'survival' package. +#' @param x Predictor of interest within the survival model for which the consonance intervals +#' should be computed. +#' @param steps Indicates how many consonance intervals are to be calculated at various levels. +#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +#' Increasing the number substantially is not recommended as it will take longer to produce +#' all the intervals and store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant statistics should be +#' generated. The default is TRUE and generates a table which is included in the list object +#' +#' @examples +#' curve_surv <- function(data, x, steps = 10000, table = TRUE) { if (is.list(data) != TRUE) { stop("Error: 'data' must be an object with a Cox Proportional Hazards model") diff --git a/R/curve_table.R b/R/curve_table.R index 9386775..a6e6940 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,3 +1,35 @@ +#' Produce Tables For concurve Functions +#' +#' Produces publication-ready tables with relevant statistics of interest for functions produced +#' from the concurve package. +#' +#' @param data Dataframe from a concurve function to produce a table for +#' @param levels Levels of the consonance intervals or likelihood intervals that should be +#' included in the table. +#' @param type Indicates whether the table is for a consonance function or likelihood function. +#' The default is set to "c" for consonance and can be switched to "l" for likelihood. +#' @param format The format of the tables. The options include "data.frame" which is the +#' default, "tibble", "docx" (which creates a table for a word document), "pptx" (which +#' creates a table for powerpoint), "latex", (which creates a table for a TeX document), and +#' "image", which produces an image of the table. +#' +#' @examples +#' +#' library(concurve) +#' +#' GroupA <- rnorm(500) +#' GroupB <- rnorm(500) +#' +#' RandomData <- data.frame(GroupA, GroupB) +#' +#' (intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default")) +#' +#' (z <- curve_table(intervalsdf[[1]], format = "data.frame")) +#' (z <- curve_table(intervalsdf[[1]], format = "tibble")) +#' (z <- curve_table(intervalsdf, format = "docx")) +#' (z <- curve_table(intervalsdf, format = "pptx")) +#' (z <- curve_table(intervalsdf[[1]], format = "latex")) +#' (z <- curve_table(intervalsdf[[1]], format = "image")) curve_table <- function(data, levels, type = "c", format = "data.frame") { if (type == "c") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 8079c4d..a6f8225 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -1,3 +1,69 @@ +#' Plots the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 +#' +#' Takes the dataframe produced by the interval functions and plots the p-values/s-values, +#' consonance (confidence) levels, and the interval estimates to produce a p-value/s-value +#' function using ggplot2 graphics. +#' +#' @param data The dataframe produced by one of the interval functions in which the intervals +#' are stored. +#' @param type Choose whether to plot a "consonance" function, a "surprisal" function or +#' "likelihood". The default option is set to "c". The type must be set in quotes, for example +#' ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance +#' distribution function, and "cd" for the consonance density function, "l1" for relative +#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. +#' @param measure Indicates whether the object has a log transformation or is normal/default. +#' The default setting is "default". If the measure is set to "ratio", it will take +#' logarithmically transformed values and convert them back to normal values in the dataframe. +#' This is typically a setting used for binary outcomes and their measures such as risk ratios, +#' hazard ratios, and odds ratios. +#' @param levels Indicates which interval levels should be plotted on the function. By default +#' it is set to 0.95 to plot the 95% interval on the consonance function, but more levels can +#' be plotted by using the c() function for example, levels = c(0.50, 0.75, 0.95). +#' @param nullvalue Indicates whether the null value for the measure should be plotted. +#' By default, it is set to FALSE, meaning it will not be plotted as a vertical line. +#' Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " +#' default" and a vertical line at 1 when the measure is set to "ratio". +#' For example, ggconcurve(type = "consonance", data = df, measure = "ratio", +#' nullvalue = "present"). This feature is not yet available for surprisal functions. +#' @param position Determines the orientation of the P-value (consonance) function. +#' By default, it is set to "pyramid", meaning the p-value function will stand right side up, +#' like a pyramid. However, it can also be inverted via the option "inverted". +#' This will also change the sequence of the y-axes to match the orientation. +#' This can be set as such, ggconcurve(type = "c", data = df, position = "inverted"). +#' @param title A custom title for the graph. By default, it is set to "Consonance Function". +#' In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +#' data = x, title = "Custom Title"). +#' @param subtitle A custom subtitle for the graph. By default, it is set to "The function +#' contains consonance/confidence intervals at every level and the P-values." In order to set a +#' subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, +#' subtitle = "Custom Subtitle"). +#' @param xaxis A custom x-axis title for the graph. By default, it is set to "Range of Values. +#' In order to set a x-axis title, it must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). +#' @param yaxis A custom y-axis title for the graph. By default, it is set to "Consonance Level". +#' In order to set a y-axis title, it must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). +#' @param color Item that allows the user to choose the color of the points and the ribbons in +#' the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +#' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). +#' @param fill Item that allows the user to choose the color of the ribbons in the graph. +#' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, fill = "#333333"). +#' @return Plot with intervals at every consonance level graphed with their corresponding +#' p-values and compatibility levels. +#' +#' @examples +#' # Simulate random data +#' +#' library(concurve) +#' +#' GroupA <- rnorm(500) +#' GroupB <- rnorm(500) +#' +#' RandomData <- data.frame(GroupA, GroupB) +#' +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +#' (function1 <- ggconcurve(type = "consonance", intervalsdf)) ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nullvalue = FALSE, position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", diff --git a/R/plot_compare.R b/R/plot_compare.R index 88d6d28..ed774c2 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -1,3 +1,67 @@ +#' Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 +#' +#' Compares the p-value/s-value, and likelihood functions using ggplot2 graphics. +#' +#' @param data1 The first dataframe produced by one of the interval functions in which the +#' intervals are stored. +#' @param data2 The second dataframe produced by one of the interval functions in which the +#' intervals are stored. +#' @param type Choose whether to plot a "consonance" function, a "surprisal" function or +#' "likelihood". The default option is set to "c". The type must be set in quotes, +#' for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" +#' for the consonance distribution function, and "cd" for the consonance density function, "l1" for relative +#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. +#' @param measure Indicates whether the object has a log transformation or is normal/default. +#' The default setting is "default". If the measure is set to "ratio", it will take +#' logarithmically transformed values and convert them back to normal values in the dataframe. +#' This is typically a setting used for binary outcomes and their measures such as risk ratios, +#' hazard ratios, and odds ratios. +#' @param nullvalue Indicates whether the null value for the measure should be plotted. +#' By default, it is set to FALSE, meaning it will not be plotted as a vertical line. +#' Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " +#' default" and a vertical line at 1 when the measure is set to "ratio". +#' For example, ggconcurve(type = "consonance", data = df, measure = "ratio", +#' nullvalue = "present"). This feature is not yet available for surprisal functions. +#' @param position Determines the orientation of the P-value (consonance) function. +#' By default, it is set to "pyramid", meaning the p-value function will stand right side up, +#' like a pyramid. However, it can also be inverted via the option "inverted". +#' This will also change the sequence of the y-axes to match the orientation. +#' This can be set as such, ggconcurve(type = "c", data = df, position = "inverted"). +#' @param title A custom title for the graph. By default, it is set to "Consonance Function". +#' In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +#' data = x, title = "Custom Title"). +#' @param subtitle A custom subtitle for the graph. By default, it is set to "The function +#' contains consonance/confidence intervals at every level and the P-values." In order to set +#' a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, +#' subtitle = "Custom Subtitle"). +#' @param xaxis A custom x-axis title for the graph. By default, it is set to "Range of Values. +#' In order to set a x-axis title, it must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). +#' @param yaxis A custom y-axis title for the graph. By default, it is set to +#' "Consonance Level". In order to set a y-axis title, it must be in quotes. +#' For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). +#' @param color Item that allows the user to choose the color of the points and the ribbons in +#' the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +#' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). +#' @param fill Item that allows the user to choose the color of the ribbons in the graph. +#' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, fill = "#333333"). +#' @return A plot that compares two functions. +#' @examples +#' library(concurve) +#' +#' GroupA <- rnorm(500) +#' GroupB <- rnorm(500) +#' RandomData <- data.frame(GroupA, GroupB) +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +#' GroupA2 <- rnorm(500) +#' GroupB2 <- rnorm(500) +#' RandomData2 <- data.frame(GroupA2, GroupB2) +#' model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +#' +#' randomframe <- curve_gen(model, "GroupB2") +#' +#' (plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalue = FALSE, position = "pyramid", title = "Interval Functions", subtitle = "The function displays intervals at every level.", diff --git a/inst/WORDLIST b/inst/WORDLIST index 4b8af58..0a70cd0 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,56 +1,65 @@ AJPH Amrhein -ANCOVA arXiv +bca +BCa +bcaboot +boostrap +cd Codecov DAS databrowser df dichotomization +distrbutions +docx doi dplyr estiate +eval flextable forvalues functons ggconcurve ggplot -glm GroupB Hjort -https +insertedinto intervalsdf -jstatsoft kendall KJ knitr Lifecycle -lm logarithmically lointerval mathST metafor +Metaprogramming +moreconsonance NL nullvalue +ORCID paranthesis pearson postfile +powerpoint Powerpoint +pptx ProfileLikelihood pvalue Pvalue Rdoc README -refutational relace Rothman Saltelli Scand Schweder -Softw +settingthis spearman Stata Strawderman +summarise surprisal Surprisal survminer @@ -62,11 +71,7 @@ topost Trafimow Twoway upinterval -Viechtbauer -wald -www xaxis Xie -xlab -ylab yxis +Zad diff --git a/man/concurve-package.Rd b/man/concurve-package.Rd new file mode 100644 index 0000000..918695c --- /dev/null +++ b/man/concurve-package.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/concurve-package.R +\docType{package} +\name{concurve-package} +\alias{concurve} +\alias{concurve-package} +\title{concurve: Computes and Plots Compatibility (Confidence) Intervals, + P-Values, S-Values, & Likelihood Intervals to Form Consonance, + Surprisal, & Likelihood Functions} +\description{ +\if{html}{\figure{logo.png}{options: align='right' alt='logo' width='120'}} + +Allows one to compute consonance (confidence) + intervals for various statistical tests along with their corresponding + P-values, S-values, and likelihoods. The intervals can be plotted to + create consonance, surprisal, and likelihood functions allowing one to + see what effect sizes are compatible with the test model at various + consonance levels rather than being limited to one interval estimate + such as 95%. These methods are discussed by Poole C. (1987) + , Schweder T, Hjort NL. (2002) + , Singh K, Xie M, Strawderman WE. (2007) + , Rothman KJ, Greenland S, Lash TL. (2008, + ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) + , Greenland S. (2019) + , Chow ZR, Greenland S. (2019) + , and Greenland S, Chow ZR. (2019) + . +} +\seealso{ +Useful links: +\itemize{ + \item \url{https://data.lesslikely.com/concurve/} + \item \url{https://github.com/Zadchow/concurve} + \item \url{https://lesslikely.com/} + \item Report bugs at \url{https://github.com/Zadchow/concurve/issues} +} + +} +\author{ +\strong{Maintainer}: Zad R. Chow \email{zad@lesslikely.com} (\href{https://orcid.org/0000-0003-1545-8199}{ORCID}) + +Authors: +\itemize{ + \item Andrew D. Vigotsky (\href{https://orcid.org/0000-0003-3166-0688}{ORCID}) +} + +} +\keyword{internal} diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd new file mode 100644 index 0000000..521199f --- /dev/null +++ b/man/curve_boot.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_boot.R +\name{curve_boot} +\alias{curve_boot} +\title{Generate Consonance Functions via Bootstrapping} +\usage{ +curve_boot( + data = data, + func = func, + method = "bca", + replicates = 20000, + steps = 1000, + table = TRUE +) +} +\arguments{ +\item{data}{Dataset that is being used to create a consonance function} + +\item{func}{Custom function that is used to create parameters of interest that +will be bootstrapped} + +\item{method}{The boostrap method that will be used to generate the functions. +Methods include "bca" which is the default and "t".} + +\item{replicates}{Indicates how many bootstrap replicates are to be performed. The default +is currently 20000 but more may be desirable, especially to make the functions more smooth} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce +all the intervals and store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant statistics should +be generated. The default is TRUE and generates a table which is included in the list object} +} +\value{ +A list with the dataframe of values in the first list and the table in the second +if table = TRUE +} +\description{ +Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages +to generate consonance distrbutions. +} +\examples{ +iris <- datasets::iris +foo <- function(data, indices) { + foo <- function(data, indices) { + dt <- data[indices, ] + c( + cor(dt[, 1], dt[, 2], method = "p") + ) + } + (y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000)) +} +} diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd index ab2de0b..0a097de 100644 --- a/man/curve_compare.Rd +++ b/man/curve_compare.Rd @@ -1,61 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_compare.R \name{curve_compare} \alias{curve_compare} -%- Also NEED an '\alias' for EACH other topic documented here. -\title{ -Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets -} -\description{ -%% ~~ A concise (1-5 lines) description of what the function does. ~~ -} +\title{Compares two functions and produces an AUC score to show the amount of consonance.} \usage{ -curve_compare(data1, data2, type = "consonance", plot = TRUE, ...) +curve_compare(data1, data2, type = "c", plot = TRUE, ...) } -%- maybe also 'usage' for other objects documented here. \arguments{ - \item{data1}{ -%% ~~Describe \code{data1} here~~ -} - \item{data2}{ -%% ~~Describe \code{data2} here~~ -} - \item{type}{ -%% ~~Describe \code{type} here~~ -} - \item{plot}{ -%% ~~Describe \code{plot} here~~ -} - \item{\dots}{ -%% ~~Describe \code{\dots} here~~ -} -} -\details{ -%% ~~ If necessary, more details than the description above ~~ -} -\value{ -%% ~Describe the value returned -%% If it is a LIST, use -%% \item{comp1 }{Description of 'comp1'} -%% \item{comp2 }{Description of 'comp2'} -%% ... -} -\references{ -%% ~put references to the literature/web site here ~ -} -\author{ -%% ~~who you are~~ -} -\note{ -%% ~~further notes~~ -} +\item{data1}{The first dataframe produced by one of the interval functions in which the intervals are stored.} -%% ~Make other sections like Warning with \section{Warning }{....} ~ +\item{data2}{The second dataframe produced by one of the interval functions in which the intervals are stored.} -\seealso{ -%% ~~objects to See Also as \code{\link{help}}, ~~~ +\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or +"likelihood". The default option is set to "c". The type must be set in quotes, for example +ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, "l1" for relative +likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} + +\item{plot}{by default it is set to TRUE and will use the plot_compare() function to plot the two functions} + +\item{...}{Can be used to pass further arguments to plot_compare()} +} +\description{ +Compares the p-value/s-value, and likelihood functions and computes an AUC number. } \examples{ +library(concurve) +GroupA <- rnorm(500) +GroupB <- rnorm(500) +RandomData <- data.frame(GroupA, GroupB) +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) +RandomData2 <- data.frame(GroupA2, GroupB2) +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2") +(curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) +(curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) +# } -% Add one or more standard keywords, see file 'KEYWORDS' in the -% R documentation directory. -\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") -\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd index 9a4fdfd..332e67f 100644 --- a/man/curve_corr.Rd +++ b/man/curve_corr.Rd @@ -1,55 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_corr.R \name{curve_corr} \alias{curve_corr} - -\title{Computes consonance intervals for correlations -} -\description{ -Computes consonance intervals to produce P- and S-value functions for correlational analyses -using the cor.test function in base R and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values. - -} +\title{Computes Consonance Intervals for Correlations} \usage{ -curve_corr(x, y, alternative, method, steps = 10000) +curve_corr(x, y, alternative, method, steps = 10000, table = TRUE) } - \arguments{ - \item{x}{ -A vector that contains the data for one of the variables that will be analyzed for correlational analysis. -} - \item{y}{ -A vector that contains the data for one of the variables that will be analyzed for correlational analysis. -} - \item{alternative}{ -Indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association. -} - \item{method}{ -A character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe. -} -} +\item{x}{A vector that contains the data for one of the variables that will be analyzed for correlational analysis.} -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. +\item{y}{A vector that contains the data for one of the variables that will be analyzed for correlational analysis.} -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. +\item{alternative}{Indicates the alternative hypothesis and must be one of "two.sided", +"greater" or "less". You can specify just the initial letter. "greater" corresponds to +positive association, "less" to negative association.} -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. +\item{method}{A character string indicating which correlation coefficient is to be used for +the test. One of "pearson", "kendall", or "spearman", can be abbreviated.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce all +the intervals and store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant statistics should be +generated. The default is TRUE and generates a table which is included in the list object.} } +\value{ +} +\description{ +Computes consonance intervals to produce P- and S-value functions for correlational analyses +using the cor.test function in base R and places the interval limits for each interval level +into a data frame along with the corresponding p-values and s-values. +} \examples{ - GroupA <- rnorm(50) GroupB <- rnorm(50) - -joe <- curve_corr(x = GroupA, y = GroupB, - alternative = "two.sided", method = "pearson") - +joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") tibble::tibble(joe) - } - - diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd index 07cca57..fe7d3b7 100644 --- a/man/curve_gen.Rd +++ b/man/curve_gen.Rd @@ -1,62 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_gen.R \name{curve_gen} \alias{curve_gen} - -\title{Computes consonance intervals for linear models - -} -\description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in the -selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits -for each interval level into a data frame along with the corresponding p-values and s-values. -} +\title{Computes thousands of consonance (confidence) intervals for the chosen parameter in the} \usage{ -curve_gen(model, var, method = "default", replicates = 1000, steps = 10000) +curve_gen( + model, + var, + method = "wald", + replicates = 1000, + steps = 10000, + table = TRUE, + ... +) } - \arguments{ - \item{model}{ -The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here. -} - \item{var}{ -The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced. -} - \item{method}{ -Chooses the method to be used to calculate the consonance intervals. There are currently four -methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to -compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically -what most people are familiar with when computing intervals based on the calculated standard error. -The "lm" method allows this function to be used for specific scenarios like logistic regression and -the 'glm' function. The "boot" method allows for bootstrapping at certain levels. -} - \item{replicates}{ -Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 compatibility intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe. -} -} +\item{model}{The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here.} -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. +\item{var}{The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.} -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. +\item{method}{Chooses the method to be used to calculate the consonance intervals. There are currently four} -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. +\item{replicates}{Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 compatibility intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce all +the intervals and store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant statistics should} } +\value{ +} +\description{ +Computes thousands of consonance (confidence) intervals for the chosen parameter in the +} \examples{ - # Simulate random data - GroupA <- rnorm(50) GroupB <- rnorm(50) - RandomData <- data.frame(GroupA, GroupB) - rob <- glm(GroupA ~ GroupB, data = RandomData) bob <- curve_gen(rob, "GroupB", method = "lm") - tibble::tibble(bob) - } diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd index 3fb640e..55c6c0b 100644 --- a/man/curve_lik.Rd +++ b/man/curve_lik.Rd @@ -1,76 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_lik.R \name{curve_lik} \alias{curve_lik} -%- Also NEED an '\alias' for EACH other topic documented here. -\title{Computes Likelihood Intervals for Ratio Data -%% ~~function to do ... ~~ -} -\description{ -%% ~~ A concise (1-5 lines) description of what the function does. ~~ -} +\title{Compute the Profile Likelihood Functions} \usage{ -curve_lik(point, LL, UL, measure = "ratio", steps = 10000) +curve_lik(likobject, data, table = TRUE) } -%- maybe also 'usage' for other objects documented here. \arguments{ - \item{point}{ -%% ~~Describe \code{point} here~~ -} - \item{LL}{ -%% ~~Describe \code{LL} here~~ -} - \item{UL}{ -%% ~~Describe \code{UL} here~~ -} - \item{measure}{ -%% ~~Describe \code{measure} here~~ -} - \item{steps}{ -%% ~~Describe \code{steps} here~~ -} -} -\details{ -%% ~~ If necessary, more details than the description above ~~ -} -\value{ -%% ~Describe the value returned -%% If it is a LIST, use -%% \item{comp1 }{Description of 'comp1'} -%% \item{comp2 }{Description of 'comp2'} -%% ... -} -\references{ -%% ~put references to the literature/web site here ~ -} -\author{ -%% ~~who you are~~ -} -\note{ -%% ~~further notes~~ -} +\item{likobject}{An object from the ProfileLikelihood package} -%% ~Make other sections like Warning with \section{Warning }{....} ~ +\item{data}{The dataframe that was used to create the likelihood object in the ProfileLikelihood package.} -\seealso{ -%% ~~objects to See Also as \code{\link{help}}, ~~~ +\item{table}{Indicates whether or not a table output with some relevant statistics should} +} +\description{ +Compute the Profile Likelihood Functions } \examples{ -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) - -tibble::tibble(lik1) - -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) - -tibble::tibble(lik2) - -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present")) - -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present")) - +data(dataglm) +xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) +(curve_lik(xx, dataglm)) } -% Add one or more standard keywords, see file 'KEYWORDS' in the -% R documentation directory. -\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") -\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd index 0e9b00b..8280245 100644 --- a/man/curve_mean.Rd +++ b/man/curve_mean.Rd @@ -1,66 +1,59 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_mean.R \name{curve_mean} \alias{curve_mean} - -\title{Computes consonance intervals for mean differences -} -\description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test -that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values. -} +\title{Mean Interval Consonance Function} \usage{ -curve_mean(x, y, data, paired = F, method = "default", -replicates = 1000, steps = 10000) +curve_mean( + x, + y, + data, + paired = F, + method = "default", + replicates = 1000, + steps = 10000, + table = TRUE +) } - \arguments{ - \item{x}{ -Variable that contains the data for the first group being compared. -} - \item{y}{ -Variable that contains the data for the second group being compared. -} - \item{data}{ -Data frame from which the variables are being extracted from. -} - \item{paired}{ -Indicates whether the statistical test is a paired difference test. By default, it is set to "F", -which means the function will be an unpaired statistical test comparing two independent groups. -Inserting "paired" will change the test to a paired difference test. -} - \item{method}{ -By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted -into the function call. -} - \item{replicates}{ -Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe. -} -} +\item{x}{Variable that contains the data for the first group being compared.} -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. +\item{y}{Variable that contains the data for the second group being compared.} -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. +\item{data}{Data frame from which the variables are being extracted from.} -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. -} +\item{paired}{Indicates whether the statistical test is a paired difference test. +By default, it is set to "F",which means the function will be an unpaired statistical test +comparing two independent groups.Inserting "paired" will change the test to a paired +difference test.} + +\item{method}{By default this is turned off (set to "default"), but allows for bootstrapping +if "boot" is insertedinto the function call.} + +\item{replicates}{Indicates how many bootstrap replicates are to be performed if bootstrapping +is enabled as a method.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, settingthis to 100 will produce 100 consonance intervals from 0 to 100. Setting +this to 10000 will produce moreconsonance levels. By default, it is set to 1000. Increasing +the number substantially is not recommended as it will take longer to produce all the intervals +and store them into a dataframe.} +\item{table}{Indicates whether or not a table output with some relevant statistics should +be generated. The default is TRUE and generates a table which is included in the list +object.} +} +\description{ +Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test +that compares means and places the interval limits for each interval level into a data +frame along with the corresponding p-values and s-values. +} \examples{ # Simulate random data - GroupA <- runif(100, min = 0, max = 100) GroupB <- runif(100, min = 0, max = 100) - RandomData <- data.frame(GroupA, GroupB) - bob <- curve_mean(GroupA, GroupB, RandomData) - tibble::tibble(bob) - } diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd index 0c42f81..4a7166b 100644 --- a/man/curve_meta.Rd +++ b/man/curve_meta.Rd @@ -1,48 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_meta.R \name{curve_meta} \alias{curve_meta} - -\title{Computes consonance intervals for meta-analysis data +\title{Meta-analytic Consonance Function} +\usage{ +curve_meta(x, measure = "default", steps = 10000, table = TRUE) +} +\arguments{ +\item{x}{Object where the meta-analysis parameters are stored, typically a list produced by 'metafor'} + +\item{measure}{Indicates whether the object has a log transformation or is normal/default. +The default setting is "default. If the measure is set to "ratio", it will take +logarithmically transformed values and convert them back to normal values in the dataframe. +This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce all +the intervals and store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant statistics +should be generated. The default is TRUE and generates a table which is included in the +list object.} } \description{ Computes thousands of consonance (confidence) intervals for the chosen parameter in the meta-analysis done by the metafor package and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values. } -\usage{ -curve_meta(x, measure = "default", steps = 10000) -} - -\arguments{ - \item{x}{ -Object where the meta-analysis parameters are stored, typically a list produced by 'metafor' -} - \item{measure}{ -Indicates whether the object has a log transformation or is normal/default. The default setting is "default. -If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe. -} -} - -\references{ -Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3). -https://www.jstatsoft.org/article/view/v036i03/v36i03.pdf. - -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. - -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. - -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. -} - \examples{ -# Simulate random data for two groups in two studies - +# Simulate random data for two groups in two studies GroupAData <- runif(20, min = 0, max = 100) GroupAMean <- round(mean(GroupAData), digits = 2) GroupASD <- round(sd(GroupAData), digits = 2) @@ -59,7 +48,7 @@ GroupDData <- runif(20, min = 0, max = 100) GroupDMean <- round(mean(GroupDData), digits = 2) GroupDSD <- round(sd(GroupDData), digits = 2) -# Combine the data +# Combine the data StudyName <- c("Study1", "Study2") MeanTreatment <- c(GroupAMean, GroupCMean) @@ -69,27 +58,21 @@ SDControl <- c(GroupBSD, GroupDSD) NTreatment <- c(20, 20) NControl <- c(20, 20) -metadf <- data.frame( - StudyName, MeanTreatment, MeanControl, - SDTreatment, SDControl, NTreatment, NControl -) +metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl) # Use metafor to calculate the standardized mean difference library(metafor) - -dat <- escalc( - measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, - m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf -) - +# +dat <- escalc(measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf) +# # Pool the data using a particular method. Here "FE" is the fixed-effects model - +# res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) - +# # Calculate the intervals using the metainterval function - +# metaf <- curve_meta(res) - +# tibble::tibble(metaf) } diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd index a366bab..364b898 100644 --- a/man/curve_rev.Rd +++ b/man/curve_rev.Rd @@ -1,59 +1,52 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_rev.R \name{curve_rev} \alias{curve_rev} - -\title{ -Reverse engineer consonance and surprisal functions from confidence limits and point estimates -} -\description{ -Using the confidence limits and point estimates from a dataset, one can use these estimates to -compute thousands of consonance intervals and graph the intervals to form a consonance and -surprisal function. -} +\title{Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits} \usage{ -curve_rev(point, LL, UL, measure = "default", steps = 10000) +curve_rev( + point, + LL, + UL, + type = "c", + measure = "default", + steps = 10000, + table = TRUE +) } - \arguments{ - \item{point}{ -The point estimate from an analysis. Ex: 1.20 -} - \item{LL}{ -The lower confidence limit from an analysis Ex: 1.0 -} - \item{UL}{ -The upper confidence limit from an analysis Ex: 1.4 -} - \item{measure}{ -The type of data being used. If they involve mean differences, -then the "default" option should be used, which is also the default setting. -If the data are ratios, then the "ratio" option should be used. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe. -} +\item{point}{The point estimate from an analysis. Ex: 1.20} -} +\item{LL}{The lower confidence limit from an analysis Ex: 1.0} -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. +\item{UL}{The upper confidence limit from an analysis Ex: 1.4} -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. +\item{type}{Indicates whether the produced result should be a consonance function +or a likelihood function. The default is "c" for consonance and likelihood can be set via +"l".} -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. -} +\item{measure}{The type of data being used. If they involve mean differences,} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce +all the intervals and store them into a dataframe.} +\item{table}{Indicates whether or not a table output with some relevant statistics should +be generated. The default is TRUE and generates a table which is included in the list object.} +} +\description{ +Using the confidence limits and point estimates from a dataset, one can use these +estimates to compute thousands of consonance intervals and graph the intervals to +form a consonance and surprisal function. +} \examples{ # From a real published study. Point estimate of the result was hazard ratio of 1.61 and # lower bound of the interval is 0.997 while upper bound of the interval is 2.59. - +# df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") tibble::tibble(df) - } - - diff --git a/man/curve_surv.Rd b/man/curve_surv.Rd index fa88cbd..4d32202 100644 --- a/man/curve_surv.Rd +++ b/man/curve_surv.Rd @@ -1,36 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_surv.R \name{curve_surv} \alias{curve_surv} -%- Also NEED an '\alias' for EACH other topic documented here. -\title{Produce Consonance Intervals for Survival Data +\title{Produce Consonance Intervals for Survival Data} +\usage{ +curve_surv(data, x, steps = 10000, table = TRUE) +} +\arguments{ +\item{data}{Object where the Cox model is stored, typically a list produced by the +'survival' package.} + +\item{x}{Predictor of interest within the survival model for which the consonance intervals +should be computed.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. +For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. +Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. +Increasing the number substantially is not recommended as it will take longer to produce +all the intervals and store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant statistics should be +generated. The default is TRUE and generates a table which is included in the list object} } \description{ Computes thousands of consonance (confidence) intervals for the chosen parameter in the Cox model computed by the 'survival' package and places the interval limits for each interval level into a data frame along with the corresponding p-value and s-value. } -\usage{ -curve_surv(data, x, steps = 10000) -} -%- maybe also 'usage' for other objects documented here. -\arguments{ - \item{data}{ -Object where the Cox model is stored, typically a list produced by the 'survival' package. -} - \item{x}{ -Predictor of interest within the survival model for which the consonance intervals should be computed. -} - \item{steps}{ -Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe. -} -} - -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. - -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. +\examples{ -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. } diff --git a/man/curve_table.Rd b/man/curve_table.Rd index 228ff26..9e7e616 100644 --- a/man/curve_table.Rd +++ b/man/curve_table.Rd @@ -1,51 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_table.R \name{curve_table} \alias{curve_table} -%- Also NEED an '\alias' for EACH other topic documented here. -\title{Produces a Table of Relevant Statistics from the `concurve` functions -%% ~~function to do ... ~~ -} -\description{ -%% ~~ A concise (1-5 lines) description of what the function does. ~~ -} +\title{Produce Tables For concurve Functions} \usage{ -curve_table(data, levels, type = "data.frame") +curve_table(data, levels, type = "c", format = "data.frame") } -%- maybe also 'usage' for other objects documented here. \arguments{ - \item{data}{ -%% ~~Describe \code{data} here~~ -} - \item{levels}{ -%% ~~Describe \code{levels} here~~ -} - \item{type}{ -%% ~~Describe \code{type} here~~ -} -} -\details{ -%% ~~ If necessary, more details than the description above ~~ -} -\value{ -%% ~Describe the value returned -%% If it is a LIST, use -%% \item{comp1 }{Description of 'comp1'} -%% \item{comp2 }{Description of 'comp2'} -%% ... -} -\references{ -%% ~put references to the literature/web site here ~ -} -\author{ -%% ~~who you are~~ -} -\note{ -%% ~~further notes~~ -} +\item{data}{Dataframe from a concurve function to produce a table for} + +\item{levels}{Levels of the consonance intervals or likelihood intervals that should be +included in the table.} -%% ~Make other sections like Warning with \section{Warning }{....} ~ +\item{type}{Indicates whether the table is for a consonance function or likelihood function. +The default is set to "c" for consonance and can be switched to "l" for likelihood.} -\seealso{ -%% ~~objects to See Also as \code{\link{help}}, ~~~ +\item{format}{The format of the tables. The options include "data.frame" which is the +default, "tibble", "docx" (which creates a table for a word document), "pptx" (which +creates a table for powerpoint), "latex", (which creates a table for a TeX document), and +"image", which produces an image of the table.} +} +\description{ +Produces publication-ready tables with relevant statistics of interest for functions produced +from the concurve package. } \examples{ @@ -56,18 +33,12 @@ GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) +(intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default")) -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - -(z <- curve_table(intervalsdf, type = "data.frame")) -(z <- curve_table(intervalsdf, type = "tibble")) -(z <- curve_table(intervalsdf, type = "latex")) -(z <- curve_table(intervalsdf, type = "image")) - +(z <- curve_table(intervalsdf[[1]], format = "data.frame")) +(z <- curve_table(intervalsdf[[1]], format = "tibble")) +(z <- curve_table(intervalsdf, format = "docx")) +(z <- curve_table(intervalsdf, format = "pptx")) +(z <- curve_table(intervalsdf[[1]], format = "latex")) +(z <- curve_table(intervalsdf[[1]], format = "image")) } -% Add one or more standard keywords, see file 'KEYWORDS' in the -% R documentation directory. -\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") -\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index 73f3f1c..140cf99 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -1,68 +1,91 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ggconcurve.R \name{ggconcurve} \alias{ggconcurve} - -\title{ -Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2 -} -\description{ -Takes the dataframe produced by the interval functions and plots the p-values/s-values, consonance (confidence) -levels, and the interval estimates to produce a p-value/s-value function using ggplot2 graphics. -} +\title{Plots the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2} \usage{ -ggconcurve(type, data, measure, nullvalue, position, - title, subtitle, xaxis, yaxis, color, fill) +ggconcurve( + data, + type = "c", + measure = "default", + levels = 0.95, + nullvalue = FALSE, + position = "pyramid", + title = "Interval Function", + subtitle = "The function displays intervals at every level.", + xaxis = expression(Theta ~ "Range of Values"), + yaxis = "P-value", + color = "#000000", + fill = "#239a98" +) } - \arguments{ - \item{type}{ -Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example ggconcurve(type = "surprisal") or ggconcurve(type = "consonance"). -} - \item{data}{ -The dataframe produced by one of the interval functions in which the intervals are stored. -} - \item{measure}{ -Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios. -} - \item{nullvalue}{ -Indicates whether the null value for the measure should be plotted. By default, it is set to "absent", meaning it will not be plotted as a vertical line. Changing this to "present", will plot a vertical line at 0 when the measure is set to "default" and a vertical line at 1 when the measure is set to "ratio". For example, ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions. -} - - \item{position}{ -Determines the orientation of the P-value (consonance) function By default, it is set to "pyramid", meaning the p-value function will stand right side up, like a pyramid. However, it can also be inverted via the option "inverted". This will also change the sequence of the y-axes to match the orientation. This can be set as such, ggconcurve(type = "consonance", data = df, position = "inverted") -} - - \item{title}{ -A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title"). -} - \item{subtitle}{ -A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle"). -} - - \item{xaxis}{ -A custom x-axis title for the graph. By default, it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). -} - \item{yaxis}{ -A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). -} - \item{color}{ -Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333"). -} - \item{fill}{ -Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, fill = "#333333"). -} +\item{data}{The dataframe produced by one of the interval functions in which the intervals +are stored.} + +\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or +"likelihood". The default option is set to "c". The type must be set in quotes, for example +ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, "l1" for relative +likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} + +\item{measure}{Indicates whether the object has a log transformation or is normal/default. +The default setting is "default". If the measure is set to "ratio", it will take +logarithmically transformed values and convert them back to normal values in the dataframe. +This is typically a setting used for binary outcomes and their measures such as risk ratios, +hazard ratios, and odds ratios.} + +\item{levels}{Indicates which interval levels should be plotted on the function. By default +it is set to 0.95 to plot the 95% interval on the consonance function, but more levels can +be plotted by using the c() function for example, levels = c(0.50, 0.75, 0.95).} + +\item{nullvalue}{Indicates whether the null value for the measure should be plotted. +By default, it is set to FALSE, meaning it will not be plotted as a vertical line. +Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " +default" and a vertical line at 1 when the measure is set to "ratio". +For example, ggconcurve(type = "consonance", data = df, measure = "ratio", +nullvalue = "present"). This feature is not yet available for surprisal functions.} + +\item{position}{Determines the orientation of the P-value (consonance) function. +By default, it is set to "pyramid", meaning the p-value function will stand right side up, +like a pyramid. However, it can also be inverted via the option "inverted". +This will also change the sequence of the y-axes to match the orientation. +This can be set as such, ggconcurve(type = "c", data = df, position = "inverted").} + +\item{title}{A custom title for the graph. By default, it is set to "Consonance Function". +In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +data = x, title = "Custom Title").} + +\item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function +contains consonance/confidence intervals at every level and the P-values." In order to set a +subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, +subtitle = "Custom Subtitle").} + +\item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. +In order to set a x-axis title, it must be in quotes. For example, +ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} + +\item{yaxis}{A custom y-axis title for the graph. By default, it is set to "Consonance Level". +In order to set a y-axis title, it must be in quotes. For example, +ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} + +\item{color}{Item that allows the user to choose the color of the points and the ribbons in +the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +For example, ggconcurve(type = "consonance", data = x, color = "#333333").} + +\item{fill}{Item that allows the user to choose the color of the ribbons in the graph. +By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +ggconcurve(type = "consonance", data = x, fill = "#333333").} } - \value{ -Plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels. +Plot with intervals at every consonance level graphed with their corresponding +p-values and compatibility levels. } -\references{ -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. - -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. - -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. +\description{ +Takes the dataframe produced by the interval functions and plots the p-values/s-values, +consonance (confidence) levels, and the interval estimates to produce a p-value/s-value +function using ggplot2 graphics. } - \examples{ # Simulate random data @@ -73,34 +96,6 @@ GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (function1 <- ggconcurve(type = "consonance", intervalsdf)) - -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) - -RandomData2 <- data.frame(GroupA2, GroupB2) - -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2") - -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level.")) - -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level.")) - -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) - -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level.")) - -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) - -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level.")) - } diff --git a/man/pipe.Rd b/man/pipe.Rd new file mode 100644 index 0000000..0eec752 --- /dev/null +++ b/man/pipe.Rd @@ -0,0 +1,12 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils-pipe.R +\name{\%>\%} +\alias{\%>\%} +\title{Pipe operator} +\usage{ +lhs \%>\% rhs +} +\description{ +See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +} +\keyword{internal} diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd index 0be28b9..a6b664a 100644 --- a/man/plot_compare.Rd +++ b/man/plot_compare.Rd @@ -1,130 +1,101 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/plot_compare.R \name{plot_compare} \alias{plot_compare} -%- Also NEED an '\alias' for EACH other topic documented here. -\title{Compares Different Functions on the Same Plot -%% ~~function to do ... ~~ -} -\description{ -%% ~~ A concise (1-5 lines) description of what the function does. ~~ -} +\title{Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2} \usage{ -plot_compare(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", xaxis = "Θ Range of Values", yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b") +plot_compare( + data1, + data2, + type = "c", + measure = "default", + nullvalue = FALSE, + position = "pyramid", + title = "Interval Functions", + subtitle = "The function displays intervals at every level.", + xaxis = expression(Theta ~ "Range of Values"), + yaxis = "P-value", + color = "#000000", + fill1 = "#239a98", + fill2 = "#d46c5b" +) } -%- maybe also 'usage' for other objects documented here. \arguments{ - \item{data1}{ -%% ~~Describe \code{data1} here~~ -} - \item{data2}{ -%% ~~Describe \code{data2} here~~ -} - \item{type}{ -%% ~~Describe \code{type} here~~ -} - \item{measure}{ -%% ~~Describe \code{measure} here~~ -} - \item{nullvalue}{ -%% ~~Describe \code{nullvalue} here~~ -} - \item{position}{ -%% ~~Describe \code{position} here~~ -} - \item{title}{ -%% ~~Describe \code{title} here~~ -} - \item{subtitle}{ -%% ~~Describe \code{subtitle} here~~ -} - \item{xaxis}{ -%% ~~Describe \code{xaxis} here~~ -} - \item{yaxis}{ -%% ~~Describe \code{yaxis} here~~ -} - \item{color}{ -%% ~~Describe \code{color} here~~ -} - \item{fill1}{ -%% ~~Describe \code{fill1} here~~ -} - \item{fill2}{ -%% ~~Describe \code{fill2} here~~ -} -} -\details{ -%% ~~ If necessary, more details than the description above ~~ +\item{data1}{The first dataframe produced by one of the interval functions in which the +intervals are stored.} + +\item{data2}{The second dataframe produced by one of the interval functions in which the +intervals are stored.} + +\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or +"likelihood". The default option is set to "c". The type must be set in quotes, +for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" +for the consonance distribution function, and "cd" for the consonance density function, "l1" for relative +likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} + +\item{measure}{Indicates whether the object has a log transformation or is normal/default. +The default setting is "default". If the measure is set to "ratio", it will take +logarithmically transformed values and convert them back to normal values in the dataframe. +This is typically a setting used for binary outcomes and their measures such as risk ratios, +hazard ratios, and odds ratios.} + +\item{nullvalue}{Indicates whether the null value for the measure should be plotted. +By default, it is set to FALSE, meaning it will not be plotted as a vertical line. +Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " +default" and a vertical line at 1 when the measure is set to "ratio". +For example, ggconcurve(type = "consonance", data = df, measure = "ratio", +nullvalue = "present"). This feature is not yet available for surprisal functions.} + +\item{position}{Determines the orientation of the P-value (consonance) function. +By default, it is set to "pyramid", meaning the p-value function will stand right side up, +like a pyramid. However, it can also be inverted via the option "inverted". +This will also change the sequence of the y-axes to match the orientation. +This can be set as such, ggconcurve(type = "c", data = df, position = "inverted").} + +\item{title}{A custom title for the graph. By default, it is set to "Consonance Function". +In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +data = x, title = "Custom Title").} + +\item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function +contains consonance/confidence intervals at every level and the P-values." In order to set +a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, +subtitle = "Custom Subtitle").} + +\item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. +In order to set a x-axis title, it must be in quotes. For example, +ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} + +\item{yaxis}{A custom y-axis title for the graph. By default, it is set to +"Consonance Level". In order to set a y-axis title, it must be in quotes. +For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} + +\item{color}{Item that allows the user to choose the color of the points and the ribbons in +the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +For example, ggconcurve(type = "consonance", data = x, color = "#333333").} + +\item{fill}{Item that allows the user to choose the color of the ribbons in the graph. +By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +ggconcurve(type = "consonance", data = x, fill = "#333333").} } \value{ -%% ~Describe the value returned -%% If it is a LIST, use -%% \item{comp1 }{Description of 'comp1'} -%% \item{comp2 }{Description of 'comp2'} -%% ... -} -\references{ -%% ~put references to the literature/web site here ~ -} -\author{ -%% ~~who you are~~ +A plot that compares two functions. } -\note{ -%% ~~further notes~~ -} - -%% ~Make other sections like Warning with \section{Warning }{....} ~ - -\seealso{ -%% ~~objects to See Also as \code{\link{help}}, ~~~ +\description{ +Compares the p-value/s-value, and likelihood functions using ggplot2 graphics. } \examples{ library(concurve) GroupA <- rnorm(500) GroupB <- rnorm(500) - RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values"))) - +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) - RandomData2 <- data.frame(GroupA2, GroupB2) - model <- lm(GroupA2 ~ GroupB2, data = RandomData2) randomframe <- curve_gen(model, "GroupB2") -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level.")) - -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level.")) - -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level.")) - -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level.")) - -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level.")) - -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level.")) - - -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio"))) - -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio"))) - - +(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) } -% Add one or more standard keywords, see file 'KEYWORDS' in the -% R documentation directory. -\keyword{ ~kwd1 }% use one of RShowDoc("KEYWORDS") -\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line diff --git a/man/plot_concurve.Rd b/man/plot_concurve.Rd deleted file mode 100644 index e2c8594..0000000 --- a/man/plot_concurve.Rd +++ /dev/null @@ -1,87 +0,0 @@ -\name{plot_concurve} -\alias{plot_concurve} - -\title{ -Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics. -} -\description{ -Takes the dataframe produced by the interval functions and plots the p-values, s-values, consonance (confidence) -levels, and the interval estimates to produce p- and s-value functions using base R graphics. -} -\usage{ -plot_concurve(type, data, measure, intervals, title, xlab, ylab1, ylab2, fontsize, fill) -} - -\arguments{ - \item{type}{ -Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example plot_concurve(type = "surprisal") or plot_concurve(type = "consonance"). -} - \item{data}{ -Dataframe where the results from a curve_ function is stored. -} - \item{measure}{ -Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios. -} - \item{intervals}{ -Indicates whether the limits for different consonance levels should be plotted on the graph. By default, this is set to FALSE. When set to TRUE, it will plot 50\%, 75\%, 95\%, and 99\% intervals. -} - - \item{title}{ -The title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, title = "Custom Title"). -} - - \item{xlab}{ -The label for the x-axis. By default, it is set to "Theta.". In order to set a label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, xlab = "Custom Caption"). -} - \item{ylab1}{ -A label for the y-axis on the left side of the graph. By default, it is set to "P-value." In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab1= "Custom y-axis title"). -} - \item{ylab2}{ -A label for the y-axis on the right side of the graph. By default, it is set to "Confidence Level."" In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab2= "Custom y-axis title"). -} - \item{fontsize}{ -Controls the font size in the graphs. By default, it is set to size 12.). -} - - \item{fill}{ -Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. -} -} - -\value{ -Plot with intervals at every consonance level graphed with their corresponding p- and s-values. -} -\references{ -Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: -There Is No Replication Crisis If We Don’t Expect Replication. Am Stat; 2018. - -Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values -and their resolution with S-values. Am Stat. 2018;18(136). - -Greenland S. The unconditional information in P-values, and its refutational interpretation -via S-values. 2018. - -Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. -1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x - -Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199. - -Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. - -Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008. -} - -\examples{ -# Simulate random data - -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -RandomData <- data.frame(GroupA, GroupB) -RandomModel <- lm(GroupA ~ GroupB, data = RandomData) - -intervalsdf <- curve_gen(RandomModel, "GroupB") - -plot_concurve(type = "consonance", data = intervalsdf) - -} diff --git a/man/tidyeval.Rd b/man/tidyeval.Rd new file mode 100644 index 0000000..5b97416 --- /dev/null +++ b/man/tidyeval.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils-tidy-eval.R +\name{tidyeval} +\alias{tidyeval} +\alias{expr} +\alias{enquo} +\alias{enquos} +\alias{sym} +\alias{syms} +\alias{.data} +\alias{:=} +\alias{as_name} +\alias{as_label} +\title{Tidy eval helpers} +\description{ +\itemize{ +\item \code{\link[rlang:quotation]{sym}()} creates a symbol from a string and +\code{\link[rlang:quotation]{syms}()} creates a list of symbols from a +character vector. +\item \code{\link[rlang:quotation]{enquo}()} and +\code{\link[rlang:quotation]{enquos}()} delay the execution of one or +several function arguments. \code{enquo()} returns a single quoted +expression, which is like a blueprint for the delayed computation. +\code{enquos()} returns a list of such quoted expressions. +\item \code{\link[rlang:quotation]{expr}()} quotes a new expression \emph{locally}. It +is mostly useful to build new expressions around arguments +captured with \code{\link[=enquo]{enquo()}} or \code{\link[=enquos]{enquos()}}: +\code{expr(mean(!!enquo(arg), na.rm = TRUE))}. +\item \code{\link[rlang]{as_name}()} transforms a quoted variable name +into a string. Supplying something else than a quoted variable +name is an error. + +That's unlike \code{\link[rlang]{as_label}()} which also returns +a single string but supports any kind of R object as input, +including quoted function calls and vectors. Its purpose is to +summarise that object into a single label. That label is often +suitable as a default name. + +If you don't know what a quoted expression contains (for instance +expressions captured with \code{enquo()} could be a variable +name, a call to a function, or an unquoted constant), then use +\code{as_label()}. If you know you have quoted a simple variable +name, or would like to enforce this, use \code{as_name()}. +} + +To learn more about tidy eval and how to use these tools, visit +\url{https://tidyeval.tidyverse.org} and the +\href{https://adv-r.hadley.nz/metaprogramming.html}{Metaprogramming +section} of \href{https://adv-r.hadley.nz}{Advanced R}. +} +\keyword{internal} From 8e4389b7e47ab924a749cea34a7926e89fa2bf0d Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 3 Dec 2019 02:43:55 -0500 Subject: [PATCH 14/21] updating formatting of code --- R/curve_boot.R | 30 ++++++++-------- R/curve_corr.R | 34 ++++++++++-------- R/curve_gen.R | 53 ++++++++++++++++----------- R/curve_lik.R | 8 +++-- R/curve_mean.R | 39 ++++++++++---------- R/curve_meta.R | 30 +++++++++------- R/curve_rev.R | 31 ++++++++-------- R/curve_surv.R | 28 ++++++++------- R/curve_table.R | 4 +-- R/ggconcurve.R | 88 +++++++++++++++++++++++++-------------------- R/plot_compare.R | 71 ++++++++++++++++++++---------------- man/curve_boot.Rd | 29 ++++++++------- man/curve_corr.Rd | 34 ++++++++++-------- man/curve_gen.Rd | 44 ++++++++++++++++------- man/curve_lik.Rd | 7 ++-- man/curve_mean.Rd | 47 ++++++++++++------------ man/curve_meta.Rd | 30 +++++++++------- man/curve_rev.Rd | 31 ++++++++-------- man/curve_surv.Rd | 27 +++++++------- man/curve_table.Rd | 4 +-- man/ggconcurve.Rd | 88 +++++++++++++++++++++++++-------------------- man/plot_compare.Rd | 71 ++++++++++++++++++++---------------- tests/spelling.R | 9 +++-- 23 files changed, 480 insertions(+), 357 deletions(-) diff --git a/R/curve_boot.R b/R/curve_boot.R index 591e8c5..b496912 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -3,24 +3,26 @@ #' Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages #' to generate consonance distrbutions. #' -#' @param data Dataset that is being used to create a consonance function +#' @param data Dataset that is being used to create a consonance function. #' @param func Custom function that is used to create parameters of interest that -#' will be bootstrapped +#' will be bootstrapped. #' @param method The boostrap method that will be used to generate the functions. #' Methods include "bca" which is the default and "t". #' -#' @param replicates Indicates how many bootstrap replicates are to be performed. The default -#' is currently 20000 but more may be desirable, especially to make the functions more smooth -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce -#' all the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should -#' be generated. The default is TRUE and generates a table which is included in the list object -#' -#' @return A list with the dataframe of values in the first list and the table in the second -#' if table = TRUE +#' @param replicates Indicates how many bootstrap replicates are to be performed. +#' The defaultis currently 20000 but more may be desirable, especially to make +#' the functions more smooth. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. +#' @return A list with the dataframe of values in the first list and the table +#' in the second if table = TRUE. #' #' @examples #' iris <- datasets::iris diff --git a/R/curve_corr.R b/R/curve_corr.R index a27f716..99871d6 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -1,23 +1,29 @@ #' Computes Consonance Intervals for Correlations #' -#' Computes consonance intervals to produce P- and S-value functions for correlational analyses -#' using the cor.test function in base R and places the interval limits for each interval level -#' into a data frame along with the corresponding p-values and s-values. +#' Computes consonance intervals to produce P- and S-value functions for +#' correlational analysesusing the cor.test function in base R and places the +#' interval limits for each interval levelinto a data frame along with the +#' corresponding p-values and s-values. #' -#' @param x A vector that contains the data for one of the variables that will be analyzed for correlational analysis. -#' @param y A vector that contains the data for one of the variables that will be analyzed for correlational analysis. +#' @param x A vector that contains the data for one of the variables that will +#' be analyzed for correlational analysis. +#' @param y A vector that contains the data for one of the variables that will +#' be analyzed for correlational analysis. #' @param alternative Indicates the alternative hypothesis and must be one of "two.sided", #' "greater" or "less". You can specify just the initial letter. "greater" corresponds to #' positive association, "less" to negative association. -#' @param method A character string indicating which correlation coefficient is to be used for -#' the test. One of "pearson", "kendall", or "spearman", can be abbreviated. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce all -#' the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should be -#' generated. The default is TRUE and generates a table which is included in the list object. +#' @param method A character string indicating which correlation coefficient is +#' to be used for the test. One of "pearson", "kendall", or "spearman", +#' can be abbreviated. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' #' @return #' @export diff --git a/R/curve_gen.R b/R/curve_gen.R index 01eba82..f66f8e9 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -1,26 +1,37 @@ -# General Consonance Functions Using Profile Likelihood, Wald, or the bootstrap method for linear models. +#' General Consonance Functions Using Profile Likelihood, Wald, +#' or the bootstrap method for linear models. #' -#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the -# selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits -# for each interval level into a data frame along with the corresponding p-values and s-values. -#' -#' @param model The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here. -#' @param var The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced. -#' @param method Chooses the method to be used to calculate the consonance intervals. There are currently four -# methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to -# compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically -# what most people are familiar with when computing intervals based on the calculated standard error. -# The "lm" method allows this function to be used for specific scenarios like logistic regression and -# the 'glm' function. The "boot" method allows for bootstrapping at certain levels. -#' @param replicates Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 compatibility intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce all -#' the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should -# be generated. The default is TRUE and generates a table which is included in the list object +#' Computes thousands of consonance (confidence) intervals for +#' the chosen parameter in the selected model +#' (ANOVA, ANCOVA, regression, logistic regression) and places +#' the interval limits for each interval level into a data frame along +#' with the corresponding p-values and s-values. #' +#' @param model The statistical model of interest +#' (ANOVA, regression, logistic regression) is to be indicated here. +#' @param var The variable of interest from the model (coefficients, intercept) +#' for which the intervals are to be produced. +#' @param method Chooses the method to be used to calculate the +#' consonance intervals. There are currently four methods: +#' "default", "wald", "lm", and "boot". The "default" method uses the profile +#' likelihood method to compute intervals and can be used for models created by +#' the 'lm' function. The "wald" method is typicallywhat most people are +#' familiar with when computing intervals based on the calculated standard error. +#' The "lm" method allows this function to be used for specific scenarios like +#' logistic regression and the 'glm' function. The "boot" method allows for +#' bootstrapping at certain levels. +#' @param replicates Indicates how many bootstrap replicates are to be performed. +#' The defaultis currently 20000 but more may be desirable, especially to make +#' the functions more smooth. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' @return #' @export #' diff --git a/R/curve_lik.R b/R/curve_lik.R index fddc465..ed87e83 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -1,9 +1,11 @@ #' Compute the Profile Likelihood Functions #' #' @param likobject An object from the ProfileLikelihood package -#' @param data The dataframe that was used to create the likelihood object in the ProfileLikelihood package. -#' @param table Indicates whether or not a table output with some relevant statistics should -# be generated. The default is TRUE and generates a table which is included in the list object +#' @param data The dataframe that was used to create the likelihood +#' object in the ProfileLikelihood package. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' #' @examples #' diff --git a/R/curve_mean.R b/R/curve_mean.R index 8380606..8e832b3 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -1,28 +1,31 @@ #' Mean Interval Consonance Function #' -#' Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test -#' that compares means and places the interval limits for each interval level into a data -#' frame along with the corresponding p-values and s-values. +#' Computes thousands of consonance (confidence) intervals for the chosen +#' parameter in a statistical test that compares means and places the interval +#' limits for each interval level into a data frame along with the corresponding +#' p-values and s-values. #' #' @param x Variable that contains the data for the first group being compared. #' @param y Variable that contains the data for the second group being compared. #' @param data Data frame from which the variables are being extracted from. #' @param paired Indicates whether the statistical test is a paired difference test. -#' By default, it is set to "F",which means the function will be an unpaired statistical test -#' comparing two independent groups.Inserting "paired" will change the test to a paired -#' difference test. -#' @param method By default this is turned off (set to "default"), but allows for bootstrapping -#' if "boot" is insertedinto the function call. -#' @param replicates Indicates how many bootstrap replicates are to be performed if bootstrapping -#' is enabled as a method. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, settingthis to 100 will produce 100 consonance intervals from 0 to 100. Setting -#' this to 10000 will produce moreconsonance levels. By default, it is set to 1000. Increasing -#' the number substantially is not recommended as it will take longer to produce all the intervals -#' and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should -#' be generated. The default is TRUE and generates a table which is included in the list -#' object. +#' By default, it is set to "F",which means the function will be an unpaired +#' statistical test comparing two independent groups.Inserting "paired" will +#' change the test to a paired difference test. +#' @param method By default this is turned off (set to "default"), but +#' allows for bootstrapping if "boot" is insertedinto the function call. +#' @param replicates Indicates how many bootstrap replicates are to be performed. +#' The defaultis currently 20000 but more may be desirable, especially to make +#' the functions more smooth. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' #' @examples #' diff --git a/R/curve_meta.R b/R/curve_meta.R index 3278783..4ea3526 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -1,22 +1,26 @@ #' Meta-analytic Consonance Function #' -#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the -#' meta-analysis done by the metafor package and places the interval limits for each interval level -#' into a data frame along with the corresponding p-values and s-values. +#' Computes thousands of consonance (confidence) intervals for the chosen +#' parameter in the meta-analysis done by the metafor package and places the +#' interval limits for each interval level into a data frame along with the +#' corresponding p-values and s-values. #' -#' @param x Object where the meta-analysis parameters are stored, typically a list produced by 'metafor' +#' @param x Object where the meta-analysis parameters are stored, typically a +#' list produced by 'metafor' #' @param measure Indicates whether the object has a log transformation or is normal/default. #' The default setting is "default. If the measure is set to "ratio", it will take #' logarithmically transformed values and convert them back to normal values in the dataframe. -#' This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce all -#' the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics -#' should be generated. The default is TRUE and generates a table which is included in the -#' list object. +#' This is typically a setting used for binary outcomes such as risk ratios, +#' hazard ratios, and odds ratios. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' #' @examples #' diff --git a/R/curve_rev.R b/R/curve_rev.R index c40a04d..53b8b0e 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -1,25 +1,28 @@ -#' Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits +#' Reverse Engineer Consonance / Likelihood Functions Using the Point +#' Estimate and Confidence Limits #' -#' Using the confidence limits and point estimates from a dataset, one can use these -#' estimates to compute thousands of consonance intervals and graph the intervals to -#' form a consonance and surprisal function. +#' Using the confidence limits and point estimates from a dataset, one can use +#' these estimates to compute thousands of consonance intervals and graph the +#' intervals to form a consonance and surprisal function. #' #' @param point The point estimate from an analysis. Ex: 1.20 #' @param LL The lower confidence limit from an analysis Ex: 1.0 #' @param UL The upper confidence limit from an analysis Ex: 1.4 -#' @param type Indicates whether the produced result should be a consonance function -#' or a likelihood function. The default is "c" for consonance and likelihood can be set via -#' "l". +#' @param type Indicates whether the produced result should be a consonance +#' function or a likelihood function. The default is "c" for consonance and +#' likelihood can be set via "l". #' @param measure The type of data being used. If they involve mean differences, # then the "default" option should be used, which is also the default setting. # If the data are ratios, then the "ratio" option should be used. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce -#' all the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should -#' be generated. The default is TRUE and generates a table which is included in the list object. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' #' @examples #' diff --git a/R/curve_surv.R b/R/curve_surv.R index a694a9a..cdd6013 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -1,21 +1,23 @@ #' Produce Consonance Intervals for Survival Data #' -#' Computes thousands of consonance (confidence) intervals for the chosen parameter in the -#' Cox model computed by the 'survival' package and places the interval limits for each interval level -#' into a data frame along with the corresponding p-value and s-value. +#' Computes thousands of consonance (confidence) intervals for the chosen +#' parameter in the Cox model computed by the 'survival' package and places +#' the interval limits for each interval level into a data frame along +#' with the corresponding p-value and s-value. #' #' @param data Object where the Cox model is stored, typically a list produced by the #' 'survival' package. -#' @param x Predictor of interest within the survival model for which the consonance intervals -#' should be computed. -#' @param steps Indicates how many consonance intervals are to be calculated at various levels. -#' For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -#' Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -#' Increasing the number substantially is not recommended as it will take longer to produce -#' all the intervals and store them into a dataframe. -#' @param table Indicates whether or not a table output with some relevant statistics should be -#' generated. The default is TRUE and generates a table which is included in the list object -#' +#' @param x Predictor of interest within the survival model for which the +#' consonance intervals should be computed. +#' @param steps Indicates how many consonance intervals are to be calculated at +#' various levels. For example, setting this to 100 will produce 100 consonance +#' intervals from 0 to 100. Setting this to 10000 will produce more consonance +#' levels. By default, it is set to 1000. Increasing the number substantially +#' is not recommended as it will take longer to produce all the intervals and +#' store them into a dataframe. +#' @param table Indicates whether or not a table output with some relevant +#' statistics should be generated. The default is TRUE and generates a table +#' which is included in the list object. #' @examples #' curve_surv <- function(data, x, steps = 10000, table = TRUE) { diff --git a/R/curve_table.R b/R/curve_table.R index a6e6940..e114812 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -1,7 +1,7 @@ #' Produce Tables For concurve Functions #' -#' Produces publication-ready tables with relevant statistics of interest for functions produced -#' from the concurve package. +#' Produces publication-ready tables with relevant statistics of interest +#' for functions produced from the concurve package. #' #' @param data Dataframe from a concurve function to produce a table for #' @param levels Levels of the consonance intervals or likelihood intervals that should be diff --git a/R/ggconcurve.R b/R/ggconcurve.R index a6f8225..264f2e4 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -1,50 +1,62 @@ -#' Plots the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 +#' Plots the P-Value (Consonance), S-value (Surprisal), +#' and Likelihood Function via ggplot2 #' -#' Takes the dataframe produced by the interval functions and plots the p-values/s-values, -#' consonance (confidence) levels, and the interval estimates to produce a p-value/s-value -#' function using ggplot2 graphics. +#' Takes the dataframe produced by the interval functions and +#' plots the p-values/s-values, consonance (confidence) levels, and +#' the interval estimates to produce a p-value/s-value function +#' using ggplot2 graphics. #' -#' @param data The dataframe produced by one of the interval functions in which the intervals -#' are stored. -#' @param type Choose whether to plot a "consonance" function, a "surprisal" function or -#' "likelihood". The default option is set to "c". The type must be set in quotes, for example -#' ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance -#' distribution function, and "cd" for the consonance density function, "l1" for relative -#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. -#' @param measure Indicates whether the object has a log transformation or is normal/default. -#' The default setting is "default". If the measure is set to "ratio", it will take -#' logarithmically transformed values and convert them back to normal values in the dataframe. -#' This is typically a setting used for binary outcomes and their measures such as risk ratios, +#' @param data The dataframe produced by one of the interval functions +#' in which the intervals are stored. +#' @param type Choose whether to plot a "consonance" function, a +#' "surprisal" function or "likelihood". The default option is set to "c". +#' The type must be set in quotes, for example ggconcurve (type = "s") or +#' ggconcurve(type = "c"). Other options include "pd" for the consonance +#' distribution function, and "cd" for the consonance density function, +#' "l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +#' and "d" for deviance function. +#' @param measure Indicates whether the object has a log transformation +#' or is normal/default. The default setting is "default". If the measure +#' is set to "ratio", it will take logarithmically transformed values and +#' convert them back to normal values in the dataframe. This is typically a +#' setting used for binary outcomes and their measures such as risk ratios, #' hazard ratios, and odds ratios. -#' @param levels Indicates which interval levels should be plotted on the function. By default -#' it is set to 0.95 to plot the 95% interval on the consonance function, but more levels can -#' be plotted by using the c() function for example, levels = c(0.50, 0.75, 0.95). -#' @param nullvalue Indicates whether the null value for the measure should be plotted. -#' By default, it is set to FALSE, meaning it will not be plotted as a vertical line. -#' Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " -#' default" and a vertical line at 1 when the measure is set to "ratio". -#' For example, ggconcurve(type = "consonance", data = df, measure = "ratio", -#' nullvalue = "present"). This feature is not yet available for surprisal functions. +#' @param levels Indicates which interval levels should be plotted on the function. +#' By default it is set to 0.95 to plot the 95% interval on the consonance function, +#' but more levels can be plotted by using the c() function for example, +#' levels = c(0.50, 0.75, 0.95). +#' @param nullvalue Indicates whether the null value for the measure +#' should be plotted. By default, it is set to FALSE, meaning it will not be +#' plotted as a vertical line. Changing this to TRUE, will plot a vertical +#' line at 0 when the measure is set to " default" and a vertical line at +#' 1 when the measure is set to "ratio". For example, +#' ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +#' This feature is not yet available for surprisal functions. #' @param position Determines the orientation of the P-value (consonance) function. -#' By default, it is set to "pyramid", meaning the p-value function will stand right side up, -#' like a pyramid. However, it can also be inverted via the option "inverted". -#' This will also change the sequence of the y-axes to match the orientation. -#' This can be set as such, ggconcurve(type = "c", data = df, position = "inverted"). -#' @param title A custom title for the graph. By default, it is set to "Consonance Function". -#' In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +#' By default, it is set to "pyramid", meaning the p-value function will +#' stand right side up, like a pyramid. However, it can also be inverted +#' via the option "inverted". This will also change the sequence of the +#' y-axes to match the orientation.This can be set as such, +#' ggconcurve(type = "c", data = df, position = "inverted"). +#' @param title A custom title for the graph. By default, it is +#' set to "Consonance Function". In order to set a title, it must +#' be in quotes. For example, ggconcurve(type = "consonance", #' data = x, title = "Custom Title"). -#' @param subtitle A custom subtitle for the graph. By default, it is set to "The function -#' contains consonance/confidence intervals at every level and the P-values." In order to set a -#' subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, -#' subtitle = "Custom Subtitle"). -#' @param xaxis A custom x-axis title for the graph. By default, it is set to "Range of Values. +#' @param subtitle A custom subtitle for the graph. By default, it is set +#' to "The function contains consonance/confidence intervals at every level +#' and the P-values." In order to set a subtitle, it must be in quotes. +#' For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle"). +#' @param xaxis A custom x-axis title for the graph. By default, +#' it is set to "Range of Values. #' In order to set a x-axis title, it must be in quotes. For example, #' ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). -#' @param yaxis A custom y-axis title for the graph. By default, it is set to "Consonance Level". +#' @param yaxis A custom y-axis title for the graph. By default, +#' it is set to "Consonance Level". #' In order to set a y-axis title, it must be in quotes. For example, #' ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). -#' @param color Item that allows the user to choose the color of the points and the ribbons in -#' the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +#' @param color Item that allows the user to choose the color of the points +#' and the ribbons in the graph. By default, it is set to color = "#555555". +#' The inputs must be in quotes. #' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). #' @param fill Item that allows the user to choose the color of the ribbons in the graph. #' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, diff --git a/R/plot_compare.R b/R/plot_compare.R index ed774c2..b97630d 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -6,42 +6,51 @@ #' intervals are stored. #' @param data2 The second dataframe produced by one of the interval functions in which the #' intervals are stored. -#' @param type Choose whether to plot a "consonance" function, a "surprisal" function or -#' "likelihood". The default option is set to "c". The type must be set in quotes, -#' for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" -#' for the consonance distribution function, and "cd" for the consonance density function, "l1" for relative -#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. -#' @param measure Indicates whether the object has a log transformation or is normal/default. -#' The default setting is "default". If the measure is set to "ratio", it will take -#' logarithmically transformed values and convert them back to normal values in the dataframe. -#' This is typically a setting used for binary outcomes and their measures such as risk ratios, +#' @param type Choose whether to plot a "consonance" function, a +#' "surprisal" function or "likelihood". The default option is set to "c". +#' The type must be set in quotes, for example ggconcurve (type = "s") or +#' ggconcurve(type = "c"). Other options include "pd" for the consonance +#' distribution function, and "cd" for the consonance density function, +#' "l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +#' and "d" for deviance function. +#' @param measure Indicates whether the object has a log transformation +#' or is normal/default. The default setting is "default". If the measure +#' is set to "ratio", it will take logarithmically transformed values and +#' convert them back to normal values in the dataframe. This is typically a +#' setting used for binary outcomes and their measures such as risk ratios, #' hazard ratios, and odds ratios. -#' @param nullvalue Indicates whether the null value for the measure should be plotted. -#' By default, it is set to FALSE, meaning it will not be plotted as a vertical line. -#' Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " -#' default" and a vertical line at 1 when the measure is set to "ratio". -#' For example, ggconcurve(type = "consonance", data = df, measure = "ratio", -#' nullvalue = "present"). This feature is not yet available for surprisal functions. +#' @param nullvalue Indicates whether the null value for the measure +#' should be plotted. By default, it is set to FALSE, meaning it will not be +#' plotted as a vertical line. Changing this to TRUE, will plot a vertical +#' line at 0 when the measure is set to " default" and a vertical line at +#' 1 when the measure is set to "ratio". For example, +#' ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +#' This feature is not yet available for surprisal functions. #' @param position Determines the orientation of the P-value (consonance) function. -#' By default, it is set to "pyramid", meaning the p-value function will stand right side up, -#' like a pyramid. However, it can also be inverted via the option "inverted". -#' This will also change the sequence of the y-axes to match the orientation. -#' This can be set as such, ggconcurve(type = "c", data = df, position = "inverted"). -#' @param title A custom title for the graph. By default, it is set to "Consonance Function". -#' In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +#' By default, it is set to "pyramid", meaning the p-value function will +#' stand right side up, like a pyramid. However, it can also be inverted +#' via the option "inverted". This will also change the sequence of the +#' y-axes to match the orientation.This can be set as such, +#' ggconcurve(type = "c", data = df, position = "inverted"). +#' @param title A custom title for the graph. By default, it is +#' set to "Consonance Function". In order to set a title, it must +#' be in quotes. For example, ggconcurve(type = "consonance", #' data = x, title = "Custom Title"). -#' @param subtitle A custom subtitle for the graph. By default, it is set to "The function -#' contains consonance/confidence intervals at every level and the P-values." In order to set -#' a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, -#' subtitle = "Custom Subtitle"). -#' @param xaxis A custom x-axis title for the graph. By default, it is set to "Range of Values. +#' @param subtitle A custom subtitle for the graph. By default, it is set +#' to "The function contains consonance/confidence intervals at every level +#' and the P-values." In order to set a subtitle, it must be in quotes. +#' For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle"). +#' @param xaxis A custom x-axis title for the graph. By default, +#' it is set to "Range of Values. #' In order to set a x-axis title, it must be in quotes. For example, #' ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). -#' @param yaxis A custom y-axis title for the graph. By default, it is set to -#' "Consonance Level". In order to set a y-axis title, it must be in quotes. -#' For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). -#' @param color Item that allows the user to choose the color of the points and the ribbons in -#' the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +#' @param yaxis A custom y-axis title for the graph. By default, +#' it is set to "Consonance Level". +#' In order to set a y-axis title, it must be in quotes. For example, +#' ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). +#' @param color Item that allows the user to choose the color of the points +#' and the ribbons in the graph. By default, it is set to color = "#555555". +#' The inputs must be in quotes. #' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). #' @param fill Item that allows the user to choose the color of the ribbons in the graph. #' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd index 521199f..bcfbd03 100644 --- a/man/curve_boot.Rd +++ b/man/curve_boot.Rd @@ -14,29 +14,32 @@ curve_boot( ) } \arguments{ -\item{data}{Dataset that is being used to create a consonance function} +\item{data}{Dataset that is being used to create a consonance function.} \item{func}{Custom function that is used to create parameters of interest that -will be bootstrapped} +will be bootstrapped.} \item{method}{The boostrap method that will be used to generate the functions. Methods include "bca" which is the default and "t".} -\item{replicates}{Indicates how many bootstrap replicates are to be performed. The default -is currently 20000 but more may be desirable, especially to make the functions more smooth} +\item{replicates}{Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce -all the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics should -be generated. The default is TRUE and generates a table which is included in the list object} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \value{ -A list with the dataframe of values in the first list and the table in the second -if table = TRUE +A list with the dataframe of values in the first list and the table +in the second if table = TRUE. } \description{ Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd index 332e67f..6161e9f 100644 --- a/man/curve_corr.Rd +++ b/man/curve_corr.Rd @@ -7,33 +7,39 @@ curve_corr(x, y, alternative, method, steps = 10000, table = TRUE) } \arguments{ -\item{x}{A vector that contains the data for one of the variables that will be analyzed for correlational analysis.} +\item{x}{A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.} -\item{y}{A vector that contains the data for one of the variables that will be analyzed for correlational analysis.} +\item{y}{A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.} \item{alternative}{Indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.} -\item{method}{A character string indicating which correlation coefficient is to be used for -the test. One of "pearson", "kendall", or "spearman", can be abbreviated.} +\item{method}{A character string indicating which correlation coefficient is +to be used for the test. One of "pearson", "kendall", or "spearman", +can be abbreviated.} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce all -the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics should be -generated. The default is TRUE and generates a table which is included in the list object.} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \value{ } \description{ -Computes consonance intervals to produce P- and S-value functions for correlational analyses -using the cor.test function in base R and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values. +Computes consonance intervals to produce P- and S-value functions for +correlational analysesusing the cor.test function in base R and places the +interval limits for each interval levelinto a data frame along with the +corresponding p-values and s-values. } \examples{ GroupA <- rnorm(50) diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd index fe7d3b7..5803c66 100644 --- a/man/curve_gen.Rd +++ b/man/curve_gen.Rd @@ -2,7 +2,8 @@ % Please edit documentation in R/curve_gen.R \name{curve_gen} \alias{curve_gen} -\title{Computes thousands of consonance (confidence) intervals for the chosen parameter in the} +\title{General Consonance Functions Using Profile Likelihood, Wald, +or the bootstrap method for linear models.} \usage{ curve_gen( model, @@ -15,27 +16,46 @@ curve_gen( ) } \arguments{ -\item{model}{The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here.} +\item{model}{The statistical model of interest +(ANOVA, regression, logistic regression) is to be indicated here.} -\item{var}{The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.} +\item{var}{The variable of interest from the model (coefficients, intercept) +for which the intervals are to be produced.} -\item{method}{Chooses the method to be used to calculate the consonance intervals. There are currently four} +\item{method}{Chooses the method to be used to calculate the +consonance intervals. There are currently four methods: +"default", "wald", "lm", and "boot". The "default" method uses the profile +likelihood method to compute intervals and can be used for models created by +the 'lm' function. The "wald" method is typicallywhat most people are +familiar with when computing intervals based on the calculated standard error. +The "lm" method allows this function to be used for specific scenarios like +logistic regression and the 'glm' function. The "boot" method allows for +bootstrapping at certain levels.} -\item{replicates}{Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.} +\item{replicates}{Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 compatibility intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce all -the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics should} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \value{ } \description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in the +Computes thousands of consonance (confidence) intervals for +the chosen parameter in the selected model +(ANOVA, ANCOVA, regression, logistic regression) and places +the interval limits for each interval level into a data frame along +with the corresponding p-values and s-values. } \examples{ # Simulate random data diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd index 55c6c0b..59c2ecc 100644 --- a/man/curve_lik.Rd +++ b/man/curve_lik.Rd @@ -9,9 +9,12 @@ curve_lik(likobject, data, table = TRUE) \arguments{ \item{likobject}{An object from the ProfileLikelihood package} -\item{data}{The dataframe that was used to create the likelihood object in the ProfileLikelihood package.} +\item{data}{The dataframe that was used to create the likelihood +object in the ProfileLikelihood package.} -\item{table}{Indicates whether or not a table output with some relevant statistics should} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \description{ Compute the Profile Likelihood Functions diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd index 8280245..0e33fca 100644 --- a/man/curve_mean.Rd +++ b/man/curve_mean.Rd @@ -23,30 +23,33 @@ curve_mean( \item{data}{Data frame from which the variables are being extracted from.} \item{paired}{Indicates whether the statistical test is a paired difference test. -By default, it is set to "F",which means the function will be an unpaired statistical test -comparing two independent groups.Inserting "paired" will change the test to a paired -difference test.} - -\item{method}{By default this is turned off (set to "default"), but allows for bootstrapping -if "boot" is insertedinto the function call.} - -\item{replicates}{Indicates how many bootstrap replicates are to be performed if bootstrapping -is enabled as a method.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, settingthis to 100 will produce 100 consonance intervals from 0 to 100. Setting -this to 10000 will produce moreconsonance levels. By default, it is set to 1000. Increasing -the number substantially is not recommended as it will take longer to produce all the intervals -and store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant statistics should -be generated. The default is TRUE and generates a table which is included in the list -object.} +By default, it is set to "F",which means the function will be an unpaired +statistical test comparing two independent groups.Inserting "paired" will +change the test to a paired difference test.} + +\item{method}{By default this is turned off (set to "default"), but +allows for bootstrapping if "boot" is insertedinto the function call.} + +\item{replicates}{Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test -that compares means and places the interval limits for each interval level into a data -frame along with the corresponding p-values and s-values. +Computes thousands of consonance (confidence) intervals for the chosen +parameter in a statistical test that compares means and places the interval +limits for each interval level into a data frame along with the corresponding +p-values and s-values. } \examples{ diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd index 4a7166b..a530a12 100644 --- a/man/curve_meta.Rd +++ b/man/curve_meta.Rd @@ -7,27 +7,31 @@ curve_meta(x, measure = "default", steps = 10000, table = TRUE) } \arguments{ -\item{x}{Object where the meta-analysis parameters are stored, typically a list produced by 'metafor'} +\item{x}{Object where the meta-analysis parameters are stored, typically a +list produced by 'metafor'} \item{measure}{Indicates whether the object has a log transformation or is normal/default. The default setting is "default. If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. -This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios.} +This is typically a setting used for binary outcomes such as risk ratios, +hazard ratios, and odds ratios.} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce all -the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics -should be generated. The default is TRUE and generates a table which is included in the -list object.} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in the -meta-analysis done by the metafor package and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values. +Computes thousands of consonance (confidence) intervals for the chosen +parameter in the meta-analysis done by the metafor package and places the +interval limits for each interval level into a data frame along with the +corresponding p-values and s-values. } \examples{ diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd index 364b898..a562a37 100644 --- a/man/curve_rev.Rd +++ b/man/curve_rev.Rd @@ -2,7 +2,8 @@ % Please edit documentation in R/curve_rev.R \name{curve_rev} \alias{curve_rev} -\title{Reverse Engineer Consonance / Likelihood Functions Using the Point Estimate and Confidence Limits} +\title{Reverse Engineer Consonance / Likelihood Functions Using the Point +Estimate and Confidence Limits} \usage{ curve_rev( point, @@ -21,25 +22,27 @@ curve_rev( \item{UL}{The upper confidence limit from an analysis Ex: 1.4} -\item{type}{Indicates whether the produced result should be a consonance function -or a likelihood function. The default is "c" for consonance and likelihood can be set via -"l".} +\item{type}{Indicates whether the produced result should be a consonance +function or a likelihood function. The default is "c" for consonance and +likelihood can be set via "l".} \item{measure}{The type of data being used. If they involve mean differences,} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce -all the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics should -be generated. The default is TRUE and generates a table which is included in the list object.} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \description{ -Using the confidence limits and point estimates from a dataset, one can use these -estimates to compute thousands of consonance intervals and graph the intervals to -form a consonance and surprisal function. +Using the confidence limits and point estimates from a dataset, one can use +these estimates to compute thousands of consonance intervals and graph the +intervals to form a consonance and surprisal function. } \examples{ diff --git a/man/curve_surv.Rd b/man/curve_surv.Rd index 4d32202..76ae67e 100644 --- a/man/curve_surv.Rd +++ b/man/curve_surv.Rd @@ -10,22 +10,25 @@ curve_surv(data, x, steps = 10000, table = TRUE) \item{data}{Object where the Cox model is stored, typically a list produced by the 'survival' package.} -\item{x}{Predictor of interest within the survival model for which the consonance intervals -should be computed.} +\item{x}{Predictor of interest within the survival model for which the +consonance intervals should be computed.} -\item{steps}{Indicates how many consonance intervals are to be calculated at various levels. -For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. -Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. -Increasing the number substantially is not recommended as it will take longer to produce -all the intervals and store them into a dataframe.} +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} -\item{table}{Indicates whether or not a table output with some relevant statistics should be -generated. The default is TRUE and generates a table which is included in the list object} +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} } \description{ -Computes thousands of consonance (confidence) intervals for the chosen parameter in the -Cox model computed by the 'survival' package and places the interval limits for each interval level -into a data frame along with the corresponding p-value and s-value. +Computes thousands of consonance (confidence) intervals for the chosen +parameter in the Cox model computed by the 'survival' package and places +the interval limits for each interval level into a data frame along +with the corresponding p-value and s-value. } \examples{ diff --git a/man/curve_table.Rd b/man/curve_table.Rd index 9e7e616..41b6d4a 100644 --- a/man/curve_table.Rd +++ b/man/curve_table.Rd @@ -21,8 +21,8 @@ creates a table for powerpoint), "latex", (which creates a table for a TeX docum "image", which produces an image of the table.} } \description{ -Produces publication-ready tables with relevant statistics of interest for functions produced -from the concurve package. +Produces publication-ready tables with relevant statistics of interest +for functions produced from the concurve package. } \examples{ diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index 140cf99..1893ac2 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -2,7 +2,8 @@ % Please edit documentation in R/ggconcurve.R \name{ggconcurve} \alias{ggconcurve} -\title{Plots the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2} +\title{Plots the P-Value (Consonance), S-value (Surprisal), +and Likelihood Function via ggplot2} \usage{ ggconcurve( data, @@ -20,57 +21,67 @@ ggconcurve( ) } \arguments{ -\item{data}{The dataframe produced by one of the interval functions in which the intervals -are stored.} +\item{data}{The dataframe produced by one of the interval functions +in which the intervals are stored.} -\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or -"likelihood". The default option is set to "c". The type must be set in quotes, for example -ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, "l1" for relative -likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} +\item{type}{Choose whether to plot a "consonance" function, a +"surprisal" function or "likelihood". The default option is set to "c". +The type must be set in quotes, for example ggconcurve (type = "s") or +ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, +"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +and "d" for deviance function.} -\item{measure}{Indicates whether the object has a log transformation or is normal/default. -The default setting is "default". If the measure is set to "ratio", it will take -logarithmically transformed values and convert them back to normal values in the dataframe. -This is typically a setting used for binary outcomes and their measures such as risk ratios, +\item{measure}{Indicates whether the object has a log transformation +or is normal/default. The default setting is "default". If the measure +is set to "ratio", it will take logarithmically transformed values and +convert them back to normal values in the dataframe. This is typically a +setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.} -\item{levels}{Indicates which interval levels should be plotted on the function. By default -it is set to 0.95 to plot the 95% interval on the consonance function, but more levels can -be plotted by using the c() function for example, levels = c(0.50, 0.75, 0.95).} +\item{levels}{Indicates which interval levels should be plotted on the function. +By default it is set to 0.95 to plot the 95% interval on the consonance function, +but more levels can be plotted by using the c() function for example, +levels = c(0.50, 0.75, 0.95).} -\item{nullvalue}{Indicates whether the null value for the measure should be plotted. -By default, it is set to FALSE, meaning it will not be plotted as a vertical line. -Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " -default" and a vertical line at 1 when the measure is set to "ratio". -For example, ggconcurve(type = "consonance", data = df, measure = "ratio", -nullvalue = "present"). This feature is not yet available for surprisal functions.} +\item{nullvalue}{Indicates whether the null value for the measure +should be plotted. By default, it is set to FALSE, meaning it will not be +plotted as a vertical line. Changing this to TRUE, will plot a vertical +line at 0 when the measure is set to " default" and a vertical line at +1 when the measure is set to "ratio". For example, +ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +This feature is not yet available for surprisal functions.} \item{position}{Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will stand right side up, -like a pyramid. However, it can also be inverted via the option "inverted". -This will also change the sequence of the y-axes to match the orientation. -This can be set as such, ggconcurve(type = "c", data = df, position = "inverted").} +By default, it is set to "pyramid", meaning the p-value function will +stand right side up, like a pyramid. However, it can also be inverted +via the option "inverted". This will also change the sequence of the +y-axes to match the orientation.This can be set as such, +ggconcurve(type = "c", data = df, position = "inverted").} -\item{title}{A custom title for the graph. By default, it is set to "Consonance Function". -In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +\item{title}{A custom title for the graph. By default, it is +set to "Consonance Function". In order to set a title, it must +be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title").} -\item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function -contains consonance/confidence intervals at every level and the P-values." In order to set a -subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, -subtitle = "Custom Subtitle").} +\item{subtitle}{A custom subtitle for the graph. By default, it is set +to "The function contains consonance/confidence intervals at every level +and the P-values." In order to set a subtitle, it must be in quotes. +For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").} -\item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. +\item{xaxis}{A custom x-axis title for the graph. By default, +it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} -\item{yaxis}{A custom y-axis title for the graph. By default, it is set to "Consonance Level". +\item{yaxis}{A custom y-axis title for the graph. By default, +it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} -\item{color}{Item that allows the user to choose the color of the points and the ribbons in -the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +\item{color}{Item that allows the user to choose the color of the points +and the ribbons in the graph. By default, it is set to color = "#555555". +The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333").} \item{fill}{Item that allows the user to choose the color of the ribbons in the graph. @@ -82,9 +93,10 @@ Plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels. } \description{ -Takes the dataframe produced by the interval functions and plots the p-values/s-values, -consonance (confidence) levels, and the interval estimates to produce a p-value/s-value -function using ggplot2 graphics. +Takes the dataframe produced by the interval functions and +plots the p-values/s-values, consonance (confidence) levels, and +the interval estimates to produce a p-value/s-value function +using ggplot2 graphics. } \examples{ # Simulate random data diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd index a6b664a..96b97d4 100644 --- a/man/plot_compare.Rd +++ b/man/plot_compare.Rd @@ -27,50 +27,59 @@ intervals are stored.} \item{data2}{The second dataframe produced by one of the interval functions in which the intervals are stored.} -\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or -"likelihood". The default option is set to "c". The type must be set in quotes, -for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" -for the consonance distribution function, and "cd" for the consonance density function, "l1" for relative -likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} +\item{type}{Choose whether to plot a "consonance" function, a +"surprisal" function or "likelihood". The default option is set to "c". +The type must be set in quotes, for example ggconcurve (type = "s") or +ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, +"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +and "d" for deviance function.} -\item{measure}{Indicates whether the object has a log transformation or is normal/default. -The default setting is "default". If the measure is set to "ratio", it will take -logarithmically transformed values and convert them back to normal values in the dataframe. -This is typically a setting used for binary outcomes and their measures such as risk ratios, +\item{measure}{Indicates whether the object has a log transformation +or is normal/default. The default setting is "default". If the measure +is set to "ratio", it will take logarithmically transformed values and +convert them back to normal values in the dataframe. This is typically a +setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.} -\item{nullvalue}{Indicates whether the null value for the measure should be plotted. -By default, it is set to FALSE, meaning it will not be plotted as a vertical line. -Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " -default" and a vertical line at 1 when the measure is set to "ratio". -For example, ggconcurve(type = "consonance", data = df, measure = "ratio", -nullvalue = "present"). This feature is not yet available for surprisal functions.} +\item{nullvalue}{Indicates whether the null value for the measure +should be plotted. By default, it is set to FALSE, meaning it will not be +plotted as a vertical line. Changing this to TRUE, will plot a vertical +line at 0 when the measure is set to " default" and a vertical line at +1 when the measure is set to "ratio". For example, +ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +This feature is not yet available for surprisal functions.} \item{position}{Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will stand right side up, -like a pyramid. However, it can also be inverted via the option "inverted". -This will also change the sequence of the y-axes to match the orientation. -This can be set as such, ggconcurve(type = "c", data = df, position = "inverted").} +By default, it is set to "pyramid", meaning the p-value function will +stand right side up, like a pyramid. However, it can also be inverted +via the option "inverted". This will also change the sequence of the +y-axes to match the orientation.This can be set as such, +ggconcurve(type = "c", data = df, position = "inverted").} -\item{title}{A custom title for the graph. By default, it is set to "Consonance Function". -In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", +\item{title}{A custom title for the graph. By default, it is +set to "Consonance Function". In order to set a title, it must +be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title").} -\item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function -contains consonance/confidence intervals at every level and the P-values." In order to set -a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, -subtitle = "Custom Subtitle").} +\item{subtitle}{A custom subtitle for the graph. By default, it is set +to "The function contains consonance/confidence intervals at every level +and the P-values." In order to set a subtitle, it must be in quotes. +For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").} -\item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. +\item{xaxis}{A custom x-axis title for the graph. By default, +it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} -\item{yaxis}{A custom y-axis title for the graph. By default, it is set to -"Consonance Level". In order to set a y-axis title, it must be in quotes. -For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} +\item{yaxis}{A custom y-axis title for the graph. By default, +it is set to "Consonance Level". +In order to set a y-axis title, it must be in quotes. For example, +ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} -\item{color}{Item that allows the user to choose the color of the points and the ribbons in -the graph. By default, it is set to color = "#555555". The inputs must be in quotes. +\item{color}{Item that allows the user to choose the color of the points +and the ribbons in the graph. By default, it is set to color = "#555555". +The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333").} \item{fill}{Item that allows the user to choose the color of the ribbons in the graph. diff --git a/tests/spelling.R b/tests/spelling.R index 6713838..13f77d9 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,3 +1,6 @@ -if(requireNamespace('spelling', quietly = TRUE)) - spelling::spell_check_test(vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE) +if (requireNamespace("spelling", quietly = TRUE)) { + spelling::spell_check_test( + vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE + ) +} From 83d9eb6c63f4c64597598bc95f8c644d688dbcbc Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 3 Dec 2019 04:56:07 -0500 Subject: [PATCH 15/21] Changes to documentation --- .gitignore | 1 + DESCRIPTION | 6 +- R/curve_boot.R | 24 +- R/curve_compare.R | 28 +- R/curve_corr.R | 4 +- R/curve_gen.R | 21 +- R/curve_lik.R | 6 +- R/curve_mean.R | 2 +- R/curve_meta.R | 2 +- R/curve_rev.R | 2 +- R/curve_table.R | 7 +- R/ggconcurve.R | 6 +- R/plot_compare.R | 8 +- README.Rmd | 9 +- README.md | 20 +- codemeta.json | 26 +- docs/404.html | 193 ------ docs/LICENSE-text.html | 867 ------------------------- docs/articles/examples.html | 324 --------- docs/articles/figures/function1.png | Bin 55806 -> 0 bytes docs/articles/figures/function2.png | Bin 55655 -> 0 bytes docs/articles/figures/function3.png | Bin 58415 -> 0 bytes docs/articles/figures/function4.png | Bin 57436 -> 0 bytes docs/articles/figures/function5.png | Bin 60338 -> 0 bytes docs/articles/figures/function6.png | Bin 60868 -> 0 bytes docs/articles/figures/graphmenu.png | Bin 762111 -> 0 bytes docs/articles/figures/likelihood.png | Bin 35645 -> 0 bytes docs/articles/figures/matrix.png | Bin 45799 -> 0 bytes docs/articles/figures/statacon.png | Bin 126652 -> 0 bytes docs/articles/figures/stataoutput.png | Bin 74399 -> 0 bytes docs/articles/figures/statareg.png | Bin 102214 -> 0 bytes docs/articles/figures/statascatter.png | Bin 71540 -> 0 bytes docs/articles/figures/statasurp.png | Bin 100755 -> 0 bytes docs/articles/figures/statasurplog.png | Bin 113867 -> 0 bytes docs/articles/index.html | 198 ------ docs/articles/stata.html | 302 --------- docs/authors.html | 218 ------- docs/docsearch.css | 148 ----- docs/docsearch.js | 85 --- docs/docsearch.json | 95 --- docs/extra.css | 98 --- docs/index.html | 334 ---------- docs/link.svg | 12 - 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magrittr + magrittr, + MASS Suggests: covr, roxygen2, spelling, - testthat + testthat, + rmarkdown VignetteBuilder: knitr ByteCompile: true diff --git a/R/curve_boot.R b/R/curve_boot.R index b496912..5db6e99 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -8,7 +8,6 @@ #' will be bootstrapped. #' @param method The boostrap method that will be used to generate the functions. #' Methods include "bca" which is the default and "t". -#' #' @param replicates Indicates how many bootstrap replicates are to be performed. #' The defaultis currently 20000 but more may be desirable, especially to make #' the functions more smooth. @@ -21,20 +20,25 @@ #' @param table Indicates whether or not a table output with some relevant #' statistics should be generated. The default is TRUE and generates a table #' which is included in the list object. +#' #' @return A list with the dataframe of values in the first list and the table #' in the second if table = TRUE. #' #' @examples -#' iris <- datasets::iris -#' foo <- function(data, indices) { -#' foo <- function(data, indices) { -#' dt <- data[indices, ] -#' c( -#' cor(dt[, 1], dt[, 2], method = "p") -#' ) -#' } -#' (y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000)) +#' library(Lock5Data) +#' dataz <- data(CommuteAtlanta) +#' +#' func = function(data, index) { +#' x <- as.numeric(unlist(data[1])) +#' y <- as.numeric(unlist(data[2])) +#' return(mean(x[index]) - mean(y[index])) #' } +#' +#' z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) +#' ggconcurve(data = z[[1]]) +#' ggconcurve(data = z[[2]], type = "cd") +#' + curve_boot <- function(data = data, func = func, method = "bca", replicates = 20000, steps = 1000, table = TRUE) { diff --git a/R/curve_compare.R b/R/curve_compare.R index 2ec08df..d13c474 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -2,15 +2,20 @@ #' #' Compares the p-value/s-value, and likelihood functions and computes an AUC number. #' -#' @param data1 The first dataframe produced by one of the interval functions in which the intervals are stored. -#' @param data2 The second dataframe produced by one of the interval functions in which the intervals are stored. +#' @param data1 The first dataframe produced by one of the interval functions +#' in which the intervals are stored. +#' @param data2 The second dataframe produced by one of the interval functions in +#' which the intervals are stored. #' @param type Choose whether to plot a "consonance" function, a "surprisal" function or -#' "likelihood". The default option is set to "c". The type must be set in quotes, for example -#' ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance -#' distribution function, and "cd" for the consonance density function, "l1" for relative -#' likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. -#' @param plot by default it is set to TRUE and will use the plot_compare() function to plot the two functions -#' @param ... Can be used to pass further arguments to plot_compare() +#' "likelihood". The default option is set to "c". The type must be set in quotes, +#' for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options +#' include "pd" for the consonance distribution function, and "cd" for the consonance +#' density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" +#' for likelihood and "d" for deviance function. +#' @param plot by default it is set to TRUE and will use the plot_compare() function +#' to plot the two functions. +#' @param ... Can be used to pass further arguments to plot_compare(). +#' #' @examples #' library(concurve) #' GroupA <- rnorm(500) @@ -20,11 +25,12 @@ #' GroupA2 <- rnorm(500) #' GroupB2 <- rnorm(500) #' RandomData2 <- data.frame(GroupA2, GroupB2) -#' model <- lm(GroupA2 ~ GroupB2, data = RandomData2) -#' randomframe <- curve_gen(model, "GroupB2") +#' model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) +#' randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) #' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) #' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) -#' # +#' + curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { # Consonance Function ----------------------------------------------------- diff --git a/R/curve_corr.R b/R/curve_corr.R index 99871d6..6ee3e22 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -32,7 +32,9 @@ #' GroupA <- rnorm(50) #' GroupB <- rnorm(50) #' joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") -#' tibble::tibble(joe) +#' tibble::tibble(joe[[1]]) +#' + curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { if (is.numeric(x) != TRUE) { stop("Error: 'x' must be a numeric vector") diff --git a/R/curve_gen.R b/R/curve_gen.R index f66f8e9..ac601c9 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -20,9 +20,6 @@ #' The "lm" method allows this function to be used for specific scenarios like #' logistic regression and the 'glm' function. The "boot" method allows for #' bootstrapping at certain levels. -#' @param replicates Indicates how many bootstrap replicates are to be performed. -#' The defaultis currently 20000 but more may be desirable, especially to make -#' the functions more smooth. #' @param steps Indicates how many consonance intervals are to be calculated at #' various levels. For example, setting this to 100 will produce 100 consonance #' intervals from 0 to 100. Setting this to 10000 will produce more consonance @@ -32,6 +29,7 @@ #' @param table Indicates whether or not a table output with some relevant #' statistics should be generated. The default is TRUE and generates a table #' which is included in the list object. +#' #' @return #' @export #' @@ -41,28 +39,25 @@ #' GroupB <- rnorm(50) #' RandomData <- data.frame(GroupA, GroupB) #' rob <- glm(GroupA ~ GroupB, data = RandomData) -#' bob <- curve_gen(rob, "GroupB", method = "lm") -#' tibble::tibble(bob) -curve_gen <- function(model, var, method = "wald", replicates = 1000, steps = 10000, table = TRUE, ...) { +#' bob <- curve_gen(rob, "GroupB", method = "glm") +#' tibble::tibble(bob[[1]]) + +curve_gen <- function(model, var, method = "wald", steps = 1000, table = TRUE) { if (is.character(method) != TRUE) { stop("Error: 'method' must be a character vector") } - if (is.numeric(replicates) != TRUE) { - stop("Error: 'replicates' must be a numeric vector") - } if (is.numeric(steps) != TRUE) { stop("Error: 'steps' must be a numeric vector") } - intrvls <- (0:steps) / steps + intrvls <- (1:(steps - 1)) / steps + if (method == "wald") { results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = detectCores() - 1) } else if (method == "glm") { - require(MASS) - results <- pbmclapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ], mc.cores = detectCores() - 1) } - df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_lik.R b/R/curve_lik.R index ed87e83..fd70a9a 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -8,10 +8,12 @@ #' which is included in the list object. #' #' @examples -#' +#' library(ProfileLikelihood) #' data(dataglm) #' xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) -#' (curve_lik(xx, dataglm)) +#' lik <- curve_lik(xx, dataglm) +#' tibble::tibble(lik[[1]]) + curve_lik <- function(likobject, data, table = TRUE) { values <- likobject[[1]] # theta values likelihood <- likobject[[2]] # profile likelihoods diff --git a/R/curve_mean.R b/R/curve_mean.R index 8e832b3..b91e620 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -34,7 +34,7 @@ #' GroupB <- runif(100, min = 0, max = 100) #' RandomData <- data.frame(GroupA, GroupB) #' bob <- curve_mean(GroupA, GroupB, RandomData) -#' tibble::tibble(bob) +#' tibble::tibble(bob[[1]]) curve_mean <- function(x, y, data, paired = F, method = "default", replicates = 1000, steps = 10000, table = TRUE) { if (is.numeric(x) != TRUE) { stop("Error: 'x' must be a numeric vector") diff --git a/R/curve_meta.R b/R/curve_meta.R index 4ea3526..864e7b2 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -67,7 +67,7 @@ #' # #' metaf <- curve_meta(res) #' # -#' tibble::tibble(metaf) +#' tibble::tibble(metaf[[1]]) curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { if (is.list(x) != TRUE) { stop("Error: 'x' must be a list from 'metafor'") diff --git a/R/curve_rev.R b/R/curve_rev.R index 53b8b0e..ae4bb22 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -31,7 +31,7 @@ #' # #' df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") #' -#' tibble::tibble(df) +#' tibble::tibble(df[[1]]) curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10000, table = TRUE) { diff --git a/R/curve_table.R b/R/curve_table.R index e114812..0488dc1 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -22,14 +22,13 @@ #' #' RandomData <- data.frame(GroupA, GroupB) #' -#' (intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default")) +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") #' #' (z <- curve_table(intervalsdf[[1]], format = "data.frame")) #' (z <- curve_table(intervalsdf[[1]], format = "tibble")) -#' (z <- curve_table(intervalsdf, format = "docx")) -#' (z <- curve_table(intervalsdf, format = "pptx")) #' (z <- curve_table(intervalsdf[[1]], format = "latex")) -#' (z <- curve_table(intervalsdf[[1]], format = "image")) + + curve_table <- function(data, levels, type = "c", format = "data.frame") { if (type == "c") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 264f2e4..4c2c7a5 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -60,7 +60,8 @@ #' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). #' @param fill Item that allows the user to choose the color of the ribbons in the graph. #' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, -#' ggconcurve(type = "consonance", data = x, fill = "#333333"). +#' ggconcurve(type = "c", data = x, fill = "#333333"). +#' #' @return Plot with intervals at every consonance level graphed with their corresponding #' p-values and compatibility levels. #' @@ -75,7 +76,8 @@ #' RandomData <- data.frame(GroupA, GroupB) #' #' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") -#' (function1 <- ggconcurve(type = "consonance", intervalsdf)) +#' (function1 <- ggconcurve(type = "c", intervalsdf[[1]])) + ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nullvalue = FALSE, position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", diff --git a/R/plot_compare.R b/R/plot_compare.R index b97630d..de35204 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -55,6 +55,7 @@ #' @param fill Item that allows the user to choose the color of the ribbons in the graph. #' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, #' ggconcurve(type = "consonance", data = x, fill = "#333333"). +#' #' @return A plot that compares two functions. #' @examples #' library(concurve) @@ -66,11 +67,12 @@ #' GroupA2 <- rnorm(500) #' GroupB2 <- rnorm(500) #' RandomData2 <- data.frame(GroupA2, GroupB2) -#' model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +#' model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) #' -#' randomframe <- curve_gen(model, "GroupB2") +#' randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) #' -#' (plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) +#' (plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = FALSE)) + plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalue = FALSE, position = "pyramid", title = "Interval Functions", subtitle = "The function displays intervals at every level.", diff --git a/README.Rmd b/README.Rmd index 3c815ae..9fcbcd2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -7,7 +7,6 @@ concurve | Graph Interval Functions -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/concurve)](https://cran.r-project.org/package=concurve) -[![Build -Status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) -[![Build -status](https://ci.appveyor.com/api/projects/status/v8sp9x96dap2om9s?svg=true)](https://ci.appveyor.com/project/Zadchow/concurve) +[![CRAN +status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) +[![Travis build +status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran.r-project.org/package=concurve) [![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) -[![CRAN -status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) + ----- @@ -80,6 +78,14 @@ install_github("zadchow/concurve") - ggplot2 - metafor - parallel + - MASS + - boot + - bcaboot + - compiler + - ProfileLikelihood + - pbmcapply + - rlang + - magrittr - dplyr - tidyr - knitr diff --git a/codemeta.json b/codemeta.json index 5476838..d7be97b 100644 --- a/codemeta.json +++ b/codemeta.json @@ -102,6 +102,18 @@ "url": "https://cran.r-project.org" }, "sameAs": "https://CRAN.R-project.org/package=testthat" + }, + { + "@type": "SoftwareApplication", + "identifier": "rmarkdown", + "name": "rmarkdown", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=rmarkdown" } ], "softwareRequirements": [ @@ -320,6 +332,18 @@ }, "sameAs": "https://CRAN.R-project.org/package=magrittr" }, + { + "@type": "SoftwareApplication", + "identifier": "MASS", + "name": "MASS", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=MASS" + }, { "@type": "SoftwareApplication", "identifier": "R", @@ -328,7 +352,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3867.198KB", + "fileSize": "3874.265KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index abb4ea6..0000000 --- a/docs/404.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -Page not found (404) • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
    - - - - - - - - - - - diff --git a/docs/articles/examples.html b/docs/articles/examples.html deleted file mode 100644 index ca289e2..0000000 --- a/docs/articles/examples.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - -Examples in R • concurve - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    -
    - - - - -
    -

    -Using Mean Differences

    -

    If we were to generate some random data from a normal distribution with the following code,

    - -

    and compare the means of these two “groups” using a t-test,

    -
    testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE)
    -

    we would likely see some differences, given that we have such a small sample in each group.

    -
    summary(testresults)
    -

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

    -

    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

    -

    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

    -

    It is relatively easy to produce such a function using the concurve package in R.

    -

    Install the package directly from CRAN

    -
    install.packages("concurve")
    -

    or get a more slightly up-to-date version via GitHub.

    -
    library(devtools)
    -install_github("zadchow/concurve")
    -

    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    - -

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    -
    tibble::tibble(intervalsdf)
    -

    There’s a quick look at the first few entries of our dataframe.

    -

    We can plot this data using the ggconcurve() function.

    -
    pfunction <- ggconcurve(type = "consonance", intervalsdf)
    -pfunction
    -
    - -
    -

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    -

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    -
    sfunction <- ggconcurve(type = "surprisal", intervalsdf)
    -sfunction
    -
    - -
    -

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    -
    -
    -

    -Simple Linear Models

    -

    We can also try this with other simple linear models.

    -

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    -
    GroupA2 <- rnorm(500)
    -GroupB2 <- rnorm(500)
    -
    -RandomData2 <- data.frame(GroupA2, GroupB2)
    -
    -model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    -
    -summary(model)
    -

    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    -
    randomframe <- curve_gen(model, "GroupB2")
    -tibble::tibble(randomframe)
    -

    Now that we have our data frame, we can graph our function.

    -
    ggconcurve(type = "consonance", randomframe)
    -
    - -
    -
    s <- ggconcurve(type = "surprisal", randomframe)
    -s
    -
    - -
    -

    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    - -

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    - -
    - -
    -

    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    -
    -
    -

    -Using Meta-Analysis Data

    -

    Here we present another quick example with a meta-analysis of simulated data.

    -

    First, we generate random data for two groups in two hypothetical studies

    -
    GroupAData <- runif(20, min = 0, max = 100)
    -GroupAMean <- round(mean(GroupAData), digits = 2)
    -GroupASD <- round(sd(GroupAData), digits = 2)
    -
    -GroupBData <- runif(20, min = 0, max = 100)
    -GroupBMean <- round(mean(GroupBData), digits = 2)
    -GroupBSD <- round(sd(GroupBData), digits = 2)
    -
    -GroupCData <- runif(20, min = 0, max = 100)
    -GroupCMean <- round(mean(GroupCData), digits = 2)
    -GroupCSD <- round(sd(GroupCData), digits = 2)
    -
    -GroupDData <- runif(20, min = 0, max = 100)
    -GroupDMean <- round(mean(GroupDData), digits = 2)
    -GroupDSD <- round(sd(GroupDData), digits = 2)
    -

    We can then quickly combine the data in a dataframe.

    -
    StudyName <- c("Study1", "Study2")
    -MeanTreatment <- c(GroupAMean, GroupCMean)
    -MeanControl <- c(GroupBMean, GroupDMean)
    -SDTreatment <- c(GroupASD, GroupCSD)
    -SDControl <- c(GroupBSD, GroupDSD)
    -NTreatment <- c(20, 20)
    -NControl <- c(20, 20)
    -
    -metadf <- data.frame(
    -  StudyName, MeanTreatment, MeanControl,
    -  SDTreatment, SDControl,
    -  NTreatment, NControl
    -)
    -

    Then, we’ll use metafor to calculate the standardized mean difference.

    - -

    Next, we’ll pool the data using a fixed-effects common-effects model

    - -

    Let’s look at our output.

    - -
    Fixed-Effects Model (k = 2)
    -
    -Test for Heterogeneity: 
    -Q(df = 1) = 0.61, p-val = 0.44
    -
    -Model Results:
    -
    -estimate    se  zval  pval  ci.lb  ci.ub 
    -    0.16  0.22  0.73  0.47  -0.28   0.60  
    -
    ----
    -Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    -

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    -

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    - -

    Now that we have our dataframe with every computed interval, we can plot the functions.

    -
    ggconcurve(type = "consonance", metaf)
    -
    - -
    -

    And our S-value function

    -
    ggconcurve(type = "surprisal", metaf)
    -
    - -
    -

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

    -
    -
    - - - -
    - - - - -
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z;A=bwyqaw4zb00XB(FuxB@C3UbuT^#DNLA8r!=nAopA3xAUb^v{u)G$;k(E>l3`bNz!geJ0FpIgpKg^QI*pR)5~c#-mCI zc;T?HmoLyG$8lv(?|cs$fQ$X|EtaYQ?&Z-$cl)V_wu<6PHXnGJ9U4{Q^74HN<+~}r z!oGd-3QBqxEoC)Mt?E>J8Frz<7t`Gtl=p;S2KlUd-ysNsx-A^Re3CZ)Ri(V5yxnvU zQVs;_SB^9NMHn4RtGyR5T+lc-S?t%KFyTz^zxGx)j+@IADjD8MK-kr(j!fNN zsGofbSsM&re&~HPbP{~^*%($cd#ZSMJhLG`S}N(_l7F{CTH(N{h-AH)mjvBlN@tdJ z9{vGcO;$*OmNni3X;hC+CDY1li;!R1l;AbjvbxWG1`%kbBx?Uyr=%Za7#Cg*FrX9S z`od<6+?Y=74=;!w0BnnIu*wpi9)YN&L8_yq@V4>7F`~V{ALAS-xk==$==XmZf0ygl z2YJ^q3tJUCvzTi#d9@h&2{YCi`Gv>)G-{P^M&M_vLyYiluPsK??A?eiJ7^8iJy^g4 z0(uWAeLq_)?9G#eC(KZv8_U#M1G06Wxw-lKax_}ULg>0t7&e>Rj1T@^`(}v{u#{yu__FR>C5nDc!pOrIfZzU`wbcXe=WJuHJ+Fzy?)H58pTcEXKrG7vdrjG G)IR~$U_#3P diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 91881a5..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,198 +0,0 @@ - - - - - - - - -Articles • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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    - - - - - - - - - - - diff --git a/docs/articles/stata.html b/docs/articles/stata.html deleted file mode 100644 index 37c5ef4..0000000 --- a/docs/articles/stata.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - -Using Stata • concurve - - - - - - - - - - - - - - - - - - - - -
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    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

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    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

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    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

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    First, let’s visualize the data with a scatterplot.

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    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

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    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

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    That’s what our output looks like.

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    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

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    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

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    Here’s the code that we’ll be using to achieve that.

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    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

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    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

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    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

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    • pvalue”is computed by taking 1 - “level”, which is alpha.

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    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

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    • my_new_data” is the filename that we’ve assigned to our new dataset.

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    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

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    Here are the next few major lines

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    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

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    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

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    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

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    The next line two lines

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    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

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    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

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    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

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    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

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    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

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    “post topost” is writing the results from each loop as new observations in this data structure.

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    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

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    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

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    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

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    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

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    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

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    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

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    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

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    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

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    So now, here’s what our consonance function looks like.

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    And here’s what our surprisal function looks like.

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    It looks slighly better when the y-axis is transformed logarithmically…

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    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

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    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

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    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

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    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

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    We simply replace the first line within the loop with our intended command, just as I’ve replaced

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    with

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    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

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    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

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    - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 660c3b9..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - - -Citation and Authors • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - -
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    - - -

    Chow Z, Vigotsky A (2019). -concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. -https://CRAN.R-project.org/package=concurve. -

    -
    @Manual{,
    -  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
    -  author = {Zad R. Chow and Andrew D. Vigotsky},
    -  year = {2019},
    -  url = {https://CRAN.R-project.org/package=concurve},
    -}
    - - - - - -
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    - - - - - - - - - - - diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; 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- var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/docsearch.json b/docs/docsearch.json deleted file mode 100644 index aa1fc65..0000000 --- a/docs/docsearch.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "index_name": "lesslikely-concurve", - "start_urls": [ - { - "url": "https://data.lesslikely.com/concurve//index.html", - "selectors_key": "homepage", - "tags": [ - "homepage" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//reference", - "selectors_key": "reference", - "tags": [ - "reference" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//articles", - "selectors_key": "articles", - "tags": [ - "articles" - ] - } - ], - "stop_urls": [ - "/reference/$", - "/reference/index.html", - "/articles/$", - "/articles/index.html" - ], - "sitemap_urls": [ - "https://data.lesslikely.com/concurve//sitemap.xml" - ], - "selectors": { - "homepage": { - "lvl0": { - "selector": ".contents h1", - "default_value": "concurve Home page" - }, - "lvl1": { - "selector": ".contents h2" - }, - "lvl2": { - "selector": ".contents h3", - "default_value": "Context" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li, .contents .pre" - }, - "reference": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name", - "default_value": "Argument" - }, - "lvl2": { - "selector": ".ref-arguments th", - "default_value": "Description" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li" - }, - "articles": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name" - }, - "lvl2": { - "selector": ".contents h2, .contents h3", - "default_value": "Context" - }, - "text": ".contents p, .contents li, .tempate-article .contents .pre" - } - }, - "selectors_exclude": [ - ".dont-index" - ], - "min_indexed_level": 2, - "custom_settings": { - "separatorsToIndex": "_", - "attributesToRetrieve": [ - "hierarchy", - "content", - "anchor", - "url", - "url_without_anchor" - ] - } -} - diff --git a/docs/extra.css b/docs/extra.css deleted file mode 100644 index 0f1be09..0000000 --- a/docs/extra.css +++ /dev/null @@ -1,98 +0,0 @@ -html, body { - height: 100%; - font-size: 18px; - font-family: "PT Serif"; -} - -.navbar-brand { - font-size: 16px; - font-weight: bold; -} - -.navbar-default .navbar-nav>li>a:hover, -.navbar-default .navbar-nav>li>a:focus { - background-color: #eee; -} - -.nav-pills>li.active>a, -.nav-pills>li.active>a:hover, -.nav-pills>li.active>a:focus { - background-color:#d46c5b; -} - -.label-default { - background-color:#d46c5b; -} - -p.caption { - display: none; -} - -body { - color: #444444; - line-height: 1.6; -} - -a { - color: #d46c5b; -} - -a:focus { - color: #d46c5b; -} - -h1, .h1 { - font-size: 32px; -} - -h1 small, .h1 small { - font-size: 70%; - text-transform: uppercase; - letter-spacing: 0.5px; -} - -h2, .h2 { - font-size: 28px; -} - -h3, .h3 { - font-size: 20px; -} - -h4, .h4 { - font-size: 20px; -} - -.contents h1, .contents h2, .contents h3, .contents h4 { - margin-bottom: 20px; -} - -.contents .page-header h1 { - margin-top: 25px; -} - -.contents h3, .contents h4 { - margin-top: -55px; -} - -h4.date, h4.author { - font-size: 1em; - color: #bbb; - display: inline; - padding-right: 0.5em; -} - -pre code { - font-size: 13.5px; - white-space: pre; -} - -.btn-primary { - color: #eeeeee; - background-color: #d46c5b; -} - -.btn-primary:focus { - color: #eeeeee; - background-color: #d46c5b; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 30e11c2..0000000 --- a/docs/index.html +++ /dev/null @@ -1,334 +0,0 @@ - - - - - - - -Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions • concurve - - - - - - - - - - - - - - - - - - - - -
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    -Examples

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    -Plot Publication-Ready Interval Functions

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    -(Consonance, Surprisal, Likelihood, & Deviance)

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    -Compare Interval Functions From Different Data/Studies

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    -Export Tabled Results Easily For Word, Powerpoint, & LaTeX

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    -Installation

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    -For R:

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    -Install the Package From CRAN

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    install.packages("concurve")
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    -Install the Developer Version

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    library(devtools)
    -install_github("zadchow/concurve")
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    -Check out the Examples.

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    -For Stata:

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    -Check out the Article on Using Stata for concurve.

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    -Dependencies

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    • ggplot2
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    • metafor
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    • parallel
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    -Purpose

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    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

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    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

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    Following the common (and important) warning that P-values are not hypothesis probabilities, we caution that the P-value graph is not a probability distribution: It shows compatibility of parameter values with the data, rather than plausibility or probability of those values given the data. This is not a subtle difference: compatibility is a much weaker condition than plausibility. Consider for example that complete fabrication of the data is always an explanation compatible with the data (and indeed has happened in some influential medical studies [65]), but in studies with many participants and authors involved in all aspects of data collection it becomes so implausible or improbable as to not even merit mention. We emphasize then that all the P-value ever addresses in a direct logical sense is compatibility; for hypothesis probabilities one must turn to Bayesian methods [25].

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    In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

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    From a scientific perspective, the likelihood function and p-value function provide the basis for scientific judgments by an investigator, and by other investigators who might have interest. It thus replaces a blunt yes or no decision by an opportunity for appropriate informed judgment.” - Fraser, 2019

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    Statistical software does not help you know what to compute, nor how to interpret the result. It does not offer to explain the assumptions behind methods, nor does it flag delicate or dubious assumptions. It does not warn you about multiplicity or p-hacking. It does not check whether you picked the hypothesis or analysis after looking at the data, nor track the number of analyses you tried before arriving at the one you sought to publish – another form of multiplicity. The more “powerful” and “user-friendly” the software is, the more it invites cargo-cult statistics." - Stark & Saltelli, 2018

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    -References

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    1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:1909.08579 [stat.ME]. 2019
    2. -
    3. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics. arXiv:1909.08583 [stat.ME]. 2019
    4. -
    5. Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.
    6. -
    7. Sullivan KM, Foster DA. Use of the confidence interval function._Epidemiology._ 1990;1(1):39-42.
    8. -
    9. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012.
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    11. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv [mathST]. 2007.
    12. -
    13. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332.
    14. -
    15. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There is No Replication Crisis if We Don’t Expect Replication. Am Stat. 2019
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    17. Greenland S. Valid P-values Behave Exactly As They Should. Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2019;18(136).
    18. -
    19. Fraser DAS. The p-value Function and Statistical Inference. Am Stat. 2019
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    21. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15(4):40-43.
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    - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/logo.svg b/docs/logo.svg deleted file mode 100644 index f42ed9b..0000000 --- a/docs/logo.svg +++ /dev/null @@ -1,93 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 854ed0f..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - -Changelog • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    -concurve 2.2.0 Unreleased -

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    -concurve 2.1.0 2019-09-19 -

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    -Major changes

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    • -ggconcurve() now plots both the P-values and CI level using both y-axes when the type = “consonance”. Previously, this was only possible via plot_concurve() (which uses base R graphics) because ggplot2 had a bug in its last few versions, which inhibited proper transformations in the y-axis.
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    -concurve 2.0.1 Unreleased -

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    • -plot_concurve() now has “measure” as an item which allows for ratio measures to be logarithmically scaled on the x axis. There are two options, “default”, which is set as the default option and is for mean differences, and “ratio”, which will result in the axis being logarithmically scaled.
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    -concurve 2.0 2019-07-10 -

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    • The plotpint() function which plotted consonance functions has been repackaged into ggconcurve().
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    • The plotsint() function which plotted surprisal functions has been repackaged into ggconcurve().
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    • Functions can now also be plotted with base R via the plot_concurve() function.
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    • Consonance functions can be plotted as a pyramid (right side up) or inverted (upside down) via the “position” item in ggconcurve().
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    • Null values (for means & ratios) can be plotted via the ggconcurve() function to show how much of the interval surrounds it.
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    -concurve 1.08 Unreleased -

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    -concurve 1.07 Unreleased -

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    - - - -
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index c4bf0d4..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,10 +0,0 @@ -pandoc: '2.6' -pkgdown: 1.4.1 -pkgdown_sha: ~ -articles: - examples: examples.html - stata: stata.html -urls: - reference: https://data.lesslikely.com/concurve//reference - article: https://data.lesslikely.com/concurve//articles - diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html deleted file mode 100644 index bef7a09..0000000 --- a/docs/reference/curve_compare.html +++ /dev/null @@ -1,260 +0,0 @@ - - - - - - - - -Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets — curve_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    -
    - -
    curve_compare(data1, data2, type = "consonance", plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    data1

    data2

    type

    plot

    ...

    - -

    Details

    - -

    -

    Value

    - -

    - - - -

    -

    References

    - -

    -

    Note

    - -

    -

    See also

    - -

    - -

    Examples

    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html deleted file mode 100644 index 2010f2b..0000000 --- a/docs/reference/curve_corr.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Computes consonance intervals for correlations — curve_corr • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes consonance intervals to produce P- and S-value functions for correlational analyses -using the cor.test function in base R and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_corr(x, y, alternative, method, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    x

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    y

    A vector that contains the data for one of the variables that will be analyzed for correlational analysis.

    alternative

    Indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.

    method

    A character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -joe <- curve_corr(x = GroupA, y = GroupB, - alternative = "two.sided", method = "pearson") - -tibble::tibble(joe)
    #> # A tibble: 3 x 1 -#> joe -#> <named list> -#> 1 <df[,7] [10,000 × 7]> -#> 2 <df[,1] [19,999 × 1]> -#> 3 <df[,7] [9 × 7]>
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html deleted file mode 100644 index f378826..0000000 --- a/docs/reference/curve_gen.html +++ /dev/null @@ -1,261 +0,0 @@ - - - - - - - - -Computes consonance intervals for linear models — curve_gen • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -selected model(ANOVA, ANCOVA, regression, logistic regression) and places the interval limits -for each interval level into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_gen(model, var, method = "default", replicates = 1000, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    The statistical model of interest(ANOVA, regression, logistic regression) is to be indicated here.

    var

    The variable of interest from the model (coefficients, intercept) for which the intervals are to be produced.

    method

    Chooses the method to be used to calculate the consonance intervals. There are currently four -methods: "default", "wald", "lm", and "boot". The "default" method uses the profile likelihood method to -compute intervals and can be used for models created by the 'lm' function. The "wald" method is typically -what most people are familiar with when computing intervals based on the calculated standard error. -The "lm" method allows this function to be used for specific scenarios like logistic regression and -the 'glm' function. The "boot" method allows for bootstrapping at certain levels.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 compatibility intervals from 0 to 100. Setting this to 10000 will produce more consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data - -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -RandomData <- data.frame(GroupA, GroupB) - -rob <- glm(GroupA ~ GroupB, data = RandomData) -bob <- curve_gen(rob, "GroupB", method = "lm")
    #> Error in do.call(rbind, results): object 'results' not found
    -tibble::tibble(bob)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'bob' not found
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html deleted file mode 100644 index 8e37f4b..0000000 --- a/docs/reference/curve_lik.html +++ /dev/null @@ -1,270 +0,0 @@ - - - - - - - - -Computes Likelihood Intervals for Ratio Data -<!-- %% ~~function to do ... ~~ --> — curve_lik • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    -
    - -
    curve_lik(point, LL, UL, measure = "ratio", steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    point

    LL

    UL

    measure

    steps

    - -

    Details

    - -

    -

    Value

    - -

    - - - -

    -

    References

    - -

    -

    Note

    - -

    -

    See also

    - -

    - -

    Examples

    -
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    -tibble::tibble(lik1)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'lik1' not found
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    -tibble::tibble(lik2)
    #> Error in eval_tidy(xs[[i]], unique_output): object 'lik2' not found
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present"))
    #> NULL
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present"))
    #> NULL
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html deleted file mode 100644 index 3a2e347..0000000 --- a/docs/reference/curve_mean.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - - -Computes consonance intervals for mean differences — curve_mean • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen parameter in a statistical test -that compares means and places the interval limits for each interval level into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_mean(x, y, data, paired = F, method = "default",
    -replicates = 1000, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    Variable that contains the data for the first group being compared.

    y

    Variable that contains the data for the second group being compared.

    data

    Data frame from which the variables are being extracted from.

    paired

    Indicates whether the statistical test is a paired difference test. By default, it is set to "F", -which means the function will be an unpaired statistical test comparing two independent groups. -Inserting "paired" will change the test to a paired difference test.

    method

    By default this is turned off (set to "default"), but allows for bootstrapping if "boot" is inserted -into the function call.

    replicates

    Indicates how many bootstrap replicates are to be performed if bootstrapping is enabled as a method.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data - -GroupA <- runif(100, min = 0, max = 100) -GroupB <- runif(100, min = 0, max = 100) - -RandomData <- data.frame(GroupA, GroupB) - -bob <- curve_mean(GroupA, GroupB, RandomData) - -tibble::tibble(bob)
    #> # A tibble: 3 x 1 -#> bob -#> <named list> -#> 1 <df[,7] [10,000 × 7]> -#> 2 <df[,1] [19,999 × 1]> -#> 3 <df[,7] [9 × 7]>
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html deleted file mode 100644 index 66be965..0000000 --- a/docs/reference/curve_meta.html +++ /dev/null @@ -1,298 +0,0 @@ - - - - - - - - -Computes consonance intervals for meta-analysis data — curve_meta • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -meta-analysis done by the metafor package and places the interval limits for each interval level -into a data frame along with the corresponding p-values and s-values.

    -
    - -
    curve_meta(x, measure = "default", steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    x

    Object where the meta-analysis parameters are stored, typically a list produced by 'metafor'

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default. -If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes such as risk ratios, hazard ratios, and odds ratios.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3). -https://www.jstatsoft.org/article/view/v036i03/v36i03.pdf.

    -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# Simulate random data for two groups in two studies - -GroupAData <- runif(20, min = 0, max = 100) -GroupAMean <- round(mean(GroupAData), digits = 2) -GroupASD <- round(sd(GroupAData), digits = 2) - -GroupBData <- runif(20, min = 0, max = 100) -GroupBMean <- round(mean(GroupBData), digits = 2) -GroupBSD <- round(sd(GroupBData), digits = 2) - -GroupCData <- runif(20, min = 0, max = 100) -GroupCMean <- round(mean(GroupCData), digits = 2) -GroupCSD <- round(sd(GroupCData), digits = 2) - -GroupDData <- runif(20, min = 0, max = 100) -GroupDMean <- round(mean(GroupDData), digits = 2) -GroupDSD <- round(sd(GroupDData), digits = 2) - -# Combine the data - -StudyName <- c("Study1", "Study2") -MeanTreatment <- c(GroupAMean, GroupCMean) -MeanControl <- c(GroupBMean, GroupDMean) -SDTreatment <- c(GroupASD, GroupCSD) -SDControl <- c(GroupBSD, GroupDSD) -NTreatment <- c(20, 20) -NControl <- c(20, 20) - -metadf <- data.frame( - StudyName, MeanTreatment, MeanControl, - SDTreatment, SDControl, NTreatment, NControl -) - -# Use metafor to calculate the standardized mean difference - -library(metafor)
    #> Loading required package: Matrix
    #> Loading 'metafor' package (version 2.1-0). For an overview -#> and introduction to the package please type: help(metafor).
    -dat <- escalc( - measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, - m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf -) - -# Pool the data using a particular method. Here "FE" is the fixed-effects model - -res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) - -# Calculate the intervals using the metainterval function - -metaf <- curve_meta(res) - -tibble::tibble(metaf)
    #> # A tibble: 3 x 1 -#> metaf -#> <named list> -#> 1 <df[,7] [10,000 × 7]> -#> 2 <df[,1] [19,999 × 1]> -#> 3 <df[,7] [9 × 7]>
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html deleted file mode 100644 index ff0dc62..0000000 --- a/docs/reference/curve_rev.html +++ /dev/null @@ -1,261 +0,0 @@ - - - - - - - - -Reverse engineer consonance and surprisal functions from confidence limits and point estimates — curve_rev • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Using the confidence limits and point estimates from a dataset, one can use these estimates to -compute thousands of consonance intervals and graph the intervals to form a consonance and -surprisal function.

    -
    - -
    curve_rev(point, LL, UL, measure = "default", steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    point

    The point estimate from an analysis. Ex: 1.20

    LL

    The lower confidence limit from an analysis Ex: 1.0

    UL

    The upper confidence limit from an analysis Ex: 1.4

    measure

    The type of data being used. If they involve mean differences, -then the "default" option should be used, which is also the default setting. -If the data are ratios, then the "ratio" option should be used.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    -# From a real published study. Point estimate of the result was hazard ratio of 1.61 and -# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. - -df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") - -tibble::tibble(df)
    #> # A tibble: 3 x 1 -#> df -#> <named list> -#> 1 <df[,7] [9,999 × 7]> -#> 2 <df[,1] [19,997 × 1]> -#> 3 <df[,7] [9 × 7]>
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html deleted file mode 100644 index 940f91e..0000000 --- a/docs/reference/curve_surv.html +++ /dev/null @@ -1,236 +0,0 @@ - - - - - - - - -Produce Consonance Intervals for Survival Data — curve_surv • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen parameter in the -Cox model computed by the 'survival' package and places the interval limits for each interval level -into a data frame along with the corresponding p-value and s-value.

    -
    - -
    curve_surv(data, x, steps = 10000)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    Object where the Cox model is stored, typically a list produced by the 'survival' package.

    x

    Predictor of interest within the survival model for which the consonance intervals should be computed.

    steps

    Indicates how many consonance intervals are to be calculated at various levels. For example, setting -this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more -consonance levels. By default, it is set to 1000. Increasing the number substantially is not recommended -as it will take longer to produce all the intervals and store them into a dataframe.

    - -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html deleted file mode 100644 index be88c19..0000000 --- a/docs/reference/curve_table.html +++ /dev/null @@ -1,269 +0,0 @@ - - - - - - - - -Produces a Table of Relevant Statistics from the `concurve` functions -<!-- %% ~~function to do ... ~~ --> — curve_table • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    -
    - -
    curve_table(data, levels, type = "data.frame")
    - -

    Arguments

    - - - - - - - - - - - - - - -
    data

    levels

    type

    - -

    Details

    - -

    -

    Value

    - -

    - - - -

    -

    References

    - -

    -

    Note

    - -

    -

    See also

    - -

    - -

    Examples

    -
    -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - -(z <- curve_table(intervalsdf, type = "data.frame"))
    #> Error in curve_table(intervalsdf, type = "data.frame"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "tibble"))
    #> Error in curve_table(intervalsdf, type = "tibble"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "latex"))
    #> Error in curve_table(intervalsdf, type = "latex"): object 'subdf' not found
    (z <- curve_table(intervalsdf, type = "image"))
    #> Error in curve_table(intervalsdf, type = "image"): object 'subdf' not found
    -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html deleted file mode 100644 index 6e866dd..0000000 --- a/docs/reference/defunct.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -concurve. — defunct" /> - - - - - - - - - - - - - - - - - - -
    -
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    The functions listed below are deprecated. Please use the listed alternatives.

    -
    - -
    plotpint(...)
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    -plotsint(...)
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    -meanintervals(...)
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    -metaintervals(...)
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    -genintervals(...)
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    -corrintervals(...)
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    -survintervals(...)
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    -rev_eng(...)
    - - -

    plotpint

    - - - -

    For plotpint, use ggconcurve or plot_concurve.

    -

    plotsint

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    For plotsint, use ggconcurve or plot_concurve.

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    meanintervals

    - - - -

    For meanintervals, use curve_mean.

    -

    metaintervals

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    For metaintervals, use curve_meta.

    -

    genintervals

    - - - -

    For genintervals, use curve_gen.

    -

    corrintervals

    - - - -

    For corrintervals, use curve_corr.

    -

    survintervals

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    For survintervals, use curve_surv.

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    rev_eng

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    For rev_eng, use curve_rev.

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    -
    - - - - -
    - -
    -
    - - -
    -

    Takes the dataframe produced by the interval functions and plots the p-values/s-values, consonance (confidence) -levels, and the interval estimates to produce a p-value/s-value function using ggplot2 graphics.

    -
    - -
    ggconcurve(type, data, measure, nullvalue, position,
    -           title, subtitle, xaxis, yaxis, color, fill)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example ggconcurve(type = "surprisal") or ggconcurve(type = "consonance").

    data

    The dataframe produced by one of the interval functions in which the intervals are stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    nullvalue

    Indicates whether the null value for the measure should be plotted. By default, it is set to "absent", meaning it will not be plotted as a vertical line. Changing this to "present", will plot a vertical line at 0 when the measure is set to "default" and a vertical line at 1 when the measure is set to "ratio". For example, ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function By default, it is set to "pyramid", meaning the p-value function will stand right side up, like a pyramid. However, it can also be inverted via the option "inverted". This will also change the sequence of the y-axes to match the orientation. This can be set as such, ggconcurve(type = "consonance", data = df, position = "inverted")

    title

    A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, color = "#333333").

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. For example, ggconcurve(type = "consonance", data = x, fill = "#333333").

    - -

    Value

    - -

    Plot with intervals at every consonance level graphed with their corresponding p-values and compatibility levels.

    -

    References

    - -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    # Simulate random data - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - -(function1 <- ggconcurve(type = "consonance", intervalsdf))
    #> NULL
    -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) - -RandomData2 <- data.frame(GroupA2, GroupB2) - -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2") - -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    #> NULL
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> NULL
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    -(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> NULL
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    -(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
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    All functions

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    curve_compare()

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    Compute and Compare the Area Under the Curve (AUC) for Two Different Datasets

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    curve_corr()

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    Computes consonance intervals for correlations

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    curve_gen()

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    Computes consonance intervals for linear models

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    curve_lik()

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    Computes Likelihood Intervals for Ratio Data -

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    curve_mean()

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    Computes consonance intervals for mean differences

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    curve_meta()

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    Computes consonance intervals for meta-analysis data

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    curve_rev()

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    Reverse engineer consonance and surprisal functions from confidence limits and point estimates

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    curve_surv()

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    Produce Consonance Intervals for Survival Data

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    curve_table()

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    Produces a Table of Relevant Statistics from the `concurve` functions -

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    ggconcurve()

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    Plots the P-Value (Consonance) and S-value (Surprisal) Function via ggplot2

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    plot_compare()

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    Compares Different Functions on the Same Plot -

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    plot_concurve()

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    Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics.

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    - - - - - - - - - - - diff --git a/docs/reference/plot_compare.html b/docs/reference/plot_compare.html deleted file mode 100644 index 53c45b4..0000000 --- a/docs/reference/plot_compare.html +++ /dev/null @@ -1,329 +0,0 @@ - - - - - - - - -Compares Different Functions on the Same Plot -<!-- %% ~~function to do ... ~~ --> — plot_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Arguments

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    data1

    data2

    type

    measure

    nullvalue

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    title

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    color

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    Examples

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    library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - - -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) - - -(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = expression(Theta~"= Range of Values")))
    #> NULL
    -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) - -RandomData2 <- data.frame(GroupA2, GroupB2) - -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2") - -(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
    #> NULL
    -(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
    #> NULL
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
    #> NULL
    -(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
    #> NULL
    -lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000): unused arguments (point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
    #> NULL
    -lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    #> Error in curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000): unused arguments (point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000)
    (ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", -subtitle = "The function displays deviance intervals at every level."))
    #> NULL
    - -(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> NULL
    -(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = expression(Theta~"= Hazard Ratio / Odds Ratio")))
    #> NULL
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    - - - - - - - - - - - diff --git a/docs/reference/plot_concurve.html b/docs/reference/plot_concurve.html deleted file mode 100644 index 4b2ff34..0000000 --- a/docs/reference/plot_concurve.html +++ /dev/null @@ -1,285 +0,0 @@ - - - - - - - - -Plots the P- (Consonance) and S-Value (Surprisal) Functions using base R graphics. — plot_concurve • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Takes the dataframe produced by the interval functions and plots the p-values, s-values, consonance (confidence) -levels, and the interval estimates to produce p- and s-value functions using base R graphics.

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    - -
    plot_concurve(type, data, measure, intervals, title, xlab, ylab1, ylab2, fontsize, fill)
    - -

    Arguments

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    type

    Choose whether to plot a "consonance" function or a "surprisal" function. The default option is set to "consonance". The type must be set in quotes, for example plot_concurve(type = "surprisal") or plot_concurve(type = "consonance").

    data

    Dataframe where the results from a curve_ function is stored.

    measure

    Indicates whether the object has a log transformation or is normal/default. The default setting is "default". If the measure is set to "ratio", it will take logarithmically transformed values and convert them back to normal values in the dataframe. This is typically a setting used for binary outcomes and their measures such as risk ratios, hazard ratios, and odds ratios.

    intervals

    Indicates whether the limits for different consonance levels should be plotted on the graph. By default, this is set to FALSE. When set to TRUE, it will plot 50%, 75%, 95%, and 99% intervals.

    title

    The title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, title = "Custom Title").

    xlab

    The label for the x-axis. By default, it is set to "Theta.". In order to set a label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, xlab = "Custom Caption").

    ylab1

    A label for the y-axis on the left side of the graph. By default, it is set to "P-value." In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab1= "Custom y-axis title").

    ylab2

    A label for the y-axis on the right side of the graph. By default, it is set to "Confidence Level."" In order to set a custom y-axis label, it must be in quotes. For example, plot_concurve(type = "consonance", data = data, ylab2= "Custom y-axis title").

    fontsize

    Controls the font size in the graphs. By default, it is set to size 12.).

    fill

    Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes.

    - -

    Value

    - -

    Plot with intervals at every consonance level graphed with their corresponding p- and s-values.

    -

    References

    - -

    Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: -There Is No Replication Crisis If We Don’t Expect Replication. Am Stat; 2018.

    -

    Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values -and their resolution with S-values. Am Stat. 2018;18(136).

    -

    Greenland S. The unconditional information in P-values, and its refutational interpretation -via S-values. 2018.

    -

    Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. -1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x

    -

    Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.

    -

    Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42.

    -

    Rothman KJ, Greenland S, Lash TL, Others. Modern epidemiology. 2008.

    - -

    Examples

    -
    # Simulate random data - -GroupA <- rnorm(50) -GroupB <- rnorm(50) - -RandomData <- data.frame(GroupA, GroupB) -RandomModel <- lm(GroupA ~ GroupB, data = RandomData) - -intervalsdf <- curve_gen(RandomModel, "GroupB") - -plot_concurve(type = "consonance", data = intervalsdf)
    #> Error in plot(lower.limit, pvalue, xlim = c(min(lower.limit), max(upper.limit)), panel.first = grid(ny = 0), type = "l", xlab = xlab, ylab = ylab1, main = title, yaxt = "n", las = 1): object 'lower.limit' not found
    -
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    - - - - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index ce2552b..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,51 +0,0 @@ - - - - https://data.lesslikely.com/concurve//index.html - - - https://data.lesslikely.com/concurve//reference/curve_compare.html - - - https://data.lesslikely.com/concurve//reference/curve_corr.html - - - https://data.lesslikely.com/concurve//reference/curve_gen.html - - - https://data.lesslikely.com/concurve//reference/curve_lik.html - - - https://data.lesslikely.com/concurve//reference/curve_mean.html - - - https://data.lesslikely.com/concurve//reference/curve_meta.html - - - https://data.lesslikely.com/concurve//reference/curve_rev.html - - - https://data.lesslikely.com/concurve//reference/curve_surv.html - - - https://data.lesslikely.com/concurve//reference/curve_table.html - - - https://data.lesslikely.com/concurve//reference/defunct.html - - - https://data.lesslikely.com/concurve//reference/ggconcurve.html - - - https://data.lesslikely.com/concurve//reference/plot_compare.html - - - https://data.lesslikely.com/concurve//reference/plot_concurve.html - - - https://data.lesslikely.com/concurve//articles/examples.html - - - https://data.lesslikely.com/concurve//articles/stata.html - - diff --git a/examples/examples.R b/examples/examples.R index df595a3..035007a 100644 --- a/examples/examples.R +++ b/examples/examples.R @@ -9,6 +9,8 @@ RandomData <- data.frame(GroupA, GroupB) data = RandomData, method = "default" )) +tibble::tibble(intervalsdf[[1]]) + (z <- curve_table(intervalsdf[[1]], format = "data.frame")) (z <- curve_table(intervalsdf[[1]], format = "tibble")) @@ -28,9 +30,9 @@ GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +model <- glm(GroupA2 ~ GroupB2, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2") +randomframe <- curve_gen(model, "GroupB2", method = "glm") (function2 <- ggconcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) diff --git a/inst/WORDLIST b/inst/WORDLIST index 0a70cd0..d7cd958 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,14 +1,16 @@ AJPH Amrhein +analysesusing +ANCOVA arXiv bca BCa bcaboot boostrap cd -Codecov DAS databrowser +defaultis df dichotomization distrbutions @@ -22,6 +24,7 @@ forvalues functons ggconcurve ggplot +glm GroupB Hjort insertedinto @@ -29,17 +32,20 @@ intervalsdf kendall KJ knitr +levelinto Lifecycle +lm logarithmically lointerval +magrittr mathST metafor Metaprogramming -moreconsonance NL nullvalue ORCID paranthesis +pbmcapply pearson postfile powerpoint @@ -51,11 +57,11 @@ Pvalue Rdoc README relace +rlang Rothman Saltelli Scand Schweder -settingthis spearman Stata Strawderman @@ -70,7 +76,9 @@ tidyr topost Trafimow Twoway +typicallywhat upinterval +wald xaxis Xie yxis diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd index bcfbd03..a7f6520 100644 --- a/man/curve_boot.Rd +++ b/man/curve_boot.Rd @@ -46,14 +46,17 @@ Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot to generate consonance distrbutions. } \examples{ -iris <- datasets::iris -foo <- function(data, indices) { - foo <- function(data, indices) { - dt <- data[indices, ] - c( - cor(dt[, 1], dt[, 2], method = "p") - ) - } - (y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000)) +library(Lock5Data) +dataz <- data(CommuteAtlanta) + +func = function(data, index) { + x <- as.numeric(unlist(data[1])) + y <- as.numeric(unlist(data[2])) + return(mean(x[index]) - mean(y[index])) } + +z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) +ggconcurve(data = z[[1]]) +ggconcurve(data = z[[2]], type = "cd") + } diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd index 0a097de..3b13398 100644 --- a/man/curve_compare.Rd +++ b/man/curve_compare.Rd @@ -7,19 +7,23 @@ curve_compare(data1, data2, type = "c", plot = TRUE, ...) } \arguments{ -\item{data1}{The first dataframe produced by one of the interval functions in which the intervals are stored.} +\item{data1}{The first dataframe produced by one of the interval functions +in which the intervals are stored.} -\item{data2}{The second dataframe produced by one of the interval functions in which the intervals are stored.} +\item{data2}{The second dataframe produced by one of the interval functions in +which the intervals are stored.} \item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or -"likelihood". The default option is set to "c". The type must be set in quotes, for example -ggconcurve (type = "s") or ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, "l1" for relative -likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function.} +"likelihood". The default option is set to "c". The type must be set in quotes, +for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options +include "pd" for the consonance distribution function, and "cd" for the consonance +density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" +for likelihood and "d" for deviance function.} -\item{plot}{by default it is set to TRUE and will use the plot_compare() function to plot the two functions} +\item{plot}{by default it is set to TRUE and will use the plot_compare() function +to plot the two functions.} -\item{...}{Can be used to pass further arguments to plot_compare()} +\item{...}{Can be used to pass further arguments to plot_compare().} } \description{ Compares the p-value/s-value, and likelihood functions and computes an AUC number. @@ -33,9 +37,9 @@ intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2") +model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) -# + } diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd index 6161e9f..5212048 100644 --- a/man/curve_corr.Rd +++ b/man/curve_corr.Rd @@ -45,5 +45,6 @@ corresponding p-values and s-values. GroupA <- rnorm(50) GroupB <- rnorm(50) joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") -tibble::tibble(joe) +tibble::tibble(joe[[1]]) + } diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd index 5803c66..40b155f 100644 --- a/man/curve_gen.Rd +++ b/man/curve_gen.Rd @@ -5,15 +5,7 @@ \title{General Consonance Functions Using Profile Likelihood, Wald, or the bootstrap method for linear models.} \usage{ -curve_gen( - model, - var, - method = "wald", - replicates = 1000, - steps = 10000, - table = TRUE, - ... -) +curve_gen(model, var, method = "wald", steps = 1000, table = TRUE) } \arguments{ \item{model}{The statistical model of interest @@ -32,10 +24,6 @@ The "lm" method allows this function to be used for specific scenarios like logistic regression and the 'glm' function. The "boot" method allows for bootstrapping at certain levels.} -\item{replicates}{Indicates how many bootstrap replicates are to be performed. -The defaultis currently 20000 but more may be desirable, especially to make -the functions more smooth.} - \item{steps}{Indicates how many consonance intervals are to be calculated at various levels. For example, setting this to 100 will produce 100 consonance intervals from 0 to 100. Setting this to 10000 will produce more consonance @@ -63,6 +51,6 @@ GroupA <- rnorm(50) GroupB <- rnorm(50) RandomData <- data.frame(GroupA, GroupB) rob <- glm(GroupA ~ GroupB, data = RandomData) -bob <- curve_gen(rob, "GroupB", method = "lm") -tibble::tibble(bob) +bob <- curve_gen(rob, "GroupB", method = "glm") +tibble::tibble(bob[[1]]) } diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd index 59c2ecc..9816ef7 100644 --- a/man/curve_lik.Rd +++ b/man/curve_lik.Rd @@ -20,8 +20,9 @@ which is included in the list object.} Compute the Profile Likelihood Functions } \examples{ - +library(ProfileLikelihood) data(dataglm) xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) -(curve_lik(xx, dataglm)) +lik <- curve_lik(xx, dataglm) +tibble::tibble(lik[[1]]) } diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd index 0e33fca..a51a5da 100644 --- a/man/curve_mean.Rd +++ b/man/curve_mean.Rd @@ -58,5 +58,5 @@ GroupA <- runif(100, min = 0, max = 100) GroupB <- runif(100, min = 0, max = 100) RandomData <- data.frame(GroupA, GroupB) bob <- curve_mean(GroupA, GroupB, RandomData) -tibble::tibble(bob) +tibble::tibble(bob[[1]]) } diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd index a530a12..dad11bf 100644 --- a/man/curve_meta.Rd +++ b/man/curve_meta.Rd @@ -78,5 +78,5 @@ res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE # metaf <- curve_meta(res) # -tibble::tibble(metaf) +tibble::tibble(metaf[[1]]) } diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd index a562a37..6ba3cc0 100644 --- a/man/curve_rev.Rd +++ b/man/curve_rev.Rd @@ -51,5 +51,5 @@ intervals to form a consonance and surprisal function. # df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") -tibble::tibble(df) +tibble::tibble(df[[1]]) } diff --git a/man/curve_table.Rd b/man/curve_table.Rd index 41b6d4a..b1ff840 100644 --- a/man/curve_table.Rd +++ b/man/curve_table.Rd @@ -33,12 +33,12 @@ GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -(intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default")) +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (z <- curve_table(intervalsdf[[1]], format = "data.frame")) (z <- curve_table(intervalsdf[[1]], format = "tibble")) -(z <- curve_table(intervalsdf, format = "docx")) -(z <- curve_table(intervalsdf, format = "pptx")) +(z <- curve_table(intervalsdf[[1]], format = "docx")) +(z <- curve_table(intervalsdf[[1]], format = "pptx")) (z <- curve_table(intervalsdf[[1]], format = "latex")) (z <- curve_table(intervalsdf[[1]], format = "image")) } diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index 1893ac2..24751a7 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -86,7 +86,7 @@ For example, ggconcurve(type = "consonance", data = x, color = "#333333").} \item{fill}{Item that allows the user to choose the color of the ribbons in the graph. By default, it is set to color = "#239a98". The inputs must be in quotes. For example, -ggconcurve(type = "consonance", data = x, fill = "#333333").} +ggconcurve(type = "c", data = x, fill = "#333333").} } \value{ Plot with intervals at every consonance level graphed with their corresponding @@ -109,5 +109,5 @@ GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") -(function1 <- ggconcurve(type = "consonance", intervalsdf)) +(function1 <- ggconcurve(type = "c", intervalsdf[[1]])) } diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd index 96b97d4..52b1ddb 100644 --- a/man/plot_compare.Rd +++ b/man/plot_compare.Rd @@ -102,9 +102,9 @@ intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2") +randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) -(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = TRUE)) +(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = FALSE)) } diff --git a/tests/spelling.R b/tests/spelling.R index 13f77d9..6713838 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,6 +1,3 @@ -if (requireNamespace("spelling", quietly = TRUE)) { - spelling::spell_check_test( - vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE - ) -} +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE) diff --git a/usethis.R b/usethis.R index d951099..4695718 100644 --- a/usethis.R +++ b/usethis.R @@ -1,5 +1,6 @@ # Importing other packages +use_package("MASS", "Imports", min_version = NULL) use_package("parallel", "Imports", min_version = NULL) use_package("pbmcapply", "Imports", min_version = NULL) use_package("compiler", "Imports", min_version = NULL) diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 0000000..097b241 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R From f90953aed8c0777abd25dfbe1a5d21ee152266cc Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 3 Dec 2019 08:36:45 -0500 Subject: [PATCH 16/21] updates to parallelization and R documentation --- DESCRIPTION | 4 +- LICENSE | 674 ------------------------- NAMESPACE | 2 + NEWS.md | 16 +- R/curve_boot.R | 42 +- R/curve_compare.R | 11 +- R/curve_corr.R | 8 +- R/curve_gen.R | 12 +- R/curve_lik.R | 4 +- R/curve_mean.R | 4 +- R/curve_meta.R | 4 +- R/curve_rev.R | 16 +- R/curve_surv.R | 4 +- R/curve_table.R | 6 +- R/defunct.R | 4 +- R/ggconcurve.R | 21 +- R/plot_compare.R | 28 +- R/utils-pipe.R | 11 - codemeta.json | 19 +- data/CRANpackages.rda | Bin 819 -> 0 bytes data/Depression_Ginzberg.rda | Bin 3700 -> 0 bytes data/FormatonRecall_Friendly.rda | Bin 520 -> 0 bytes data/MarijuanaArrests.rda | Bin 18563 -> 0 bytes data/ReadingComprehension_Baumann.rda | Bin 846 -> 0 bytes data/Smokingdoctors_breslow.rda | Bin 550 -> 0 bytes data/rice.rda | Bin 954 -> 0 bytes docs/404.html | 193 +++++++ docs/CONTRIBUTING.html | 238 +++++++++ docs/ISSUE_TEMPLATE.html | 199 ++++++++ docs/LICENSE.html | 415 +++++++++++++++ docs/SUPPORT.html | 208 ++++++++ docs/articles/examples.html | 324 ++++++++++++ docs/articles/figures/function1.png | Bin 0 -> 55806 bytes docs/articles/figures/function2.png | Bin 0 -> 55655 bytes docs/articles/figures/function3.png | Bin 0 -> 58415 bytes docs/articles/figures/function4.png | Bin 0 -> 57436 bytes docs/articles/figures/function5.png | Bin 0 -> 60338 bytes docs/articles/figures/function6.png | Bin 0 -> 60868 bytes docs/articles/figures/graphmenu.png | Bin 0 -> 762111 bytes docs/articles/figures/likelihood.png | Bin 0 -> 35645 bytes docs/articles/figures/matrix.png | Bin 0 -> 45799 bytes docs/articles/figures/statacon.png | Bin 0 -> 126652 bytes docs/articles/figures/stataoutput.png | Bin 0 -> 74399 bytes docs/articles/figures/statareg.png | Bin 0 -> 102214 bytes docs/articles/figures/statascatter.png | Bin 0 -> 71540 bytes docs/articles/figures/statasurp.png | Bin 0 -> 100755 bytes docs/articles/figures/statasurplog.png | Bin 0 -> 113867 bytes docs/articles/index.html | 198 ++++++++ docs/articles/stata.html | 302 +++++++++++ docs/authors.html | 218 ++++++++ docs/docsearch.css | 148 ++++++ docs/docsearch.js | 85 ++++ docs/docsearch.json | 95 ++++ docs/extra.css | 98 ++++ docs/index.html | 365 +++++++++++++ docs/link.svg | 12 + docs/logo.svg | 93 ++++ docs/news/index.html | 330 ++++++++++++ docs/pkgdown.css | 256 ++++++++++ docs/pkgdown.js | 113 +++++ docs/pkgdown.yml | 10 + docs/reference/concurve-package.html | 259 ++++++++++ docs/reference/curve_boot-1.png | Bin 0 -> 119640 bytes docs/reference/curve_boot-2.png | Bin 0 -> 118982 bytes docs/reference/curve_boot-3.png | Bin 0 -> 152322 bytes docs/reference/curve_boot.html | 276 ++++++++++ docs/reference/curve_compare-1.png | Bin 0 -> 145414 bytes docs/reference/curve_compare-2.png | Bin 0 -> 141748 bytes docs/reference/curve_compare.html | 268 ++++++++++ docs/reference/curve_corr.html | 275 ++++++++++ docs/reference/curve_gen.html | 283 +++++++++++ docs/reference/curve_lik.html | 244 +++++++++ docs/reference/curve_mean.html | 294 +++++++++++ docs/reference/curve_meta.html | 306 +++++++++++ docs/reference/curve_rev.html | 286 +++++++++++ docs/reference/curve_surv.html | 242 +++++++++ docs/reference/curve_table.html | 293 +++++++++++ docs/reference/defunct.html | 268 ++++++++++ docs/reference/figures/logo.png | Bin 0 -> 184174 bytes docs/reference/figures/logo.svg | 93 ++++ docs/reference/ggconcurve-1.png | Bin 0 -> 119053 bytes docs/reference/ggconcurve.html | 344 +++++++++++++ docs/reference/index.html | 289 +++++++++++ docs/reference/plot_compare-1.png | Bin 0 -> 139640 bytes docs/reference/plot_compare.html | 343 +++++++++++++ docs/reference/tidyeval.html | 269 ++++++++++ docs/sitemap.xml | 57 +++ examples/examples.R | 36 +- {R => examples}/plot_concurve.R | 0 man/curve_boot.Rd | 25 +- man/curve_compare.Rd | 11 +- man/curve_corr.Rd | 4 +- man/curve_gen.Rd | 7 +- man/curve_lik.Rd | 4 +- man/curve_mean.Rd | 1 + man/curve_meta.Rd | 3 +- man/curve_rev.Rd | 1 + man/curve_surv.Rd | 3 - man/curve_table.Rd | 5 +- man/defunct.Rd | 4 +- man/ggconcurve.Rd | 16 +- man/pipe.Rd | 12 - man/plot_compare.Rd | 27 +- tests/spelling.R | 9 +- tests/testthat/testdfstructure.R | 34 +- usethis.R | 1 + 106 files changed, 8825 insertions(+), 857 deletions(-) delete mode 100644 LICENSE delete mode 100644 R/utils-pipe.R delete mode 100644 data/CRANpackages.rda delete mode 100644 data/Depression_Ginzberg.rda delete mode 100644 data/FormatonRecall_Friendly.rda delete mode 100644 data/MarijuanaArrests.rda delete mode 100644 data/ReadingComprehension_Baumann.rda delete mode 100644 data/Smokingdoctors_breslow.rda delete mode 100644 data/rice.rda create mode 100644 docs/404.html create mode 100644 docs/CONTRIBUTING.html create mode 100644 docs/ISSUE_TEMPLATE.html create mode 100644 docs/LICENSE.html create mode 100644 docs/SUPPORT.html create mode 100644 docs/articles/examples.html create mode 100644 docs/articles/figures/function1.png create mode 100644 docs/articles/figures/function2.png create mode 100644 docs/articles/figures/function3.png create mode 100644 docs/articles/figures/function4.png create mode 100644 docs/articles/figures/function5.png create mode 100644 docs/articles/figures/function6.png create mode 100644 docs/articles/figures/graphmenu.png create mode 100644 docs/articles/figures/likelihood.png create mode 100644 docs/articles/figures/matrix.png create mode 100644 docs/articles/figures/statacon.png create mode 100644 docs/articles/figures/stataoutput.png create mode 100644 docs/articles/figures/statareg.png create mode 100644 docs/articles/figures/statascatter.png create mode 100644 docs/articles/figures/statasurp.png create mode 100644 docs/articles/figures/statasurplog.png create mode 100644 docs/articles/index.html create mode 100644 docs/articles/stata.html create mode 100644 docs/authors.html create mode 100644 docs/docsearch.css create mode 100644 docs/docsearch.js create mode 100644 docs/docsearch.json create mode 100644 docs/extra.css create mode 100644 docs/index.html create mode 100644 docs/link.svg create mode 100644 docs/logo.svg create mode 100644 docs/news/index.html create mode 100644 docs/pkgdown.css create mode 100644 docs/pkgdown.js create mode 100644 docs/pkgdown.yml create mode 100644 docs/reference/concurve-package.html create mode 100644 docs/reference/curve_boot-1.png create mode 100644 docs/reference/curve_boot-2.png create mode 100644 docs/reference/curve_boot-3.png create mode 100644 docs/reference/curve_boot.html create mode 100644 docs/reference/curve_compare-1.png create mode 100644 docs/reference/curve_compare-2.png create mode 100644 docs/reference/curve_compare.html create mode 100644 docs/reference/curve_corr.html create mode 100644 docs/reference/curve_gen.html create mode 100644 docs/reference/curve_lik.html create mode 100644 docs/reference/curve_mean.html create mode 100644 docs/reference/curve_meta.html create mode 100644 docs/reference/curve_rev.html create mode 100644 docs/reference/curve_surv.html create mode 100644 docs/reference/curve_table.html create mode 100644 docs/reference/defunct.html create mode 100644 docs/reference/figures/logo.png create mode 100644 docs/reference/figures/logo.svg create mode 100644 docs/reference/ggconcurve-1.png create mode 100644 docs/reference/ggconcurve.html create mode 100644 docs/reference/index.html create mode 100644 docs/reference/plot_compare-1.png create mode 100644 docs/reference/plot_compare.html create mode 100644 docs/reference/tidyeval.html create mode 100644 docs/sitemap.xml rename {R => examples}/plot_concurve.R (100%) delete mode 100644 man/pipe.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 980ebd8..2548bdc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,8 +54,8 @@ Imports: survminer, tibble, tidyr, - magrittr, - MASS + MASS, + methods Suggests: covr, roxygen2, diff --git a/LICENSE b/LICENSE deleted file mode 100644 index f288702..0000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/NAMESPACE b/NAMESPACE index d6ecf46..be80c66 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,6 +17,8 @@ import(metafor) import(scales) importFrom("stats", "coef", "confint", "confint.default", "confint.lm", "lm", "quantile", "t.test", "cor.test", "qnorm") + importFrom("methods", "is") + importFrom("stats", "approxfun", "integrate") importFrom("graphics", "axis", "par", "polygon", "text") importFrom("stats", "logLik", "model.frame", "model.matrix", "model.response", "na.fail") diff --git a/NEWS.md b/NEWS.md index 70d9cd7..655d10f 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,18 @@ -# concurve 2.2.0 +# concurve 3.0 + +## Major changes + +* `ggconcurve()` plots confidence (consonance) distributions, densities, and likelihood functions. +* `curve_compare()` compares two functions and calculates the area between the curve. +* `plot_compare() `allows two separate functions to be plotted and compared simultaneously. +* `curve_table()` produces publication-ready tables of relevant statistics. +* `curve_boot()` uses bootstrapping to approximate the consonance functions. +* `curve_lik()` produces likelihood functions by transforming the objects from the `ProfileLikelihood` package. + +## Minor changes + +* All functions now provide progress on how long it will take to complete the task. +* Interval widths are now provided as measures of precision. # concurve 2.1.0 diff --git a/R/curve_boot.R b/R/curve_boot.R index 5db6e99..d8cfc04 100644 --- a/R/curve_boot.R +++ b/R/curve_boot.R @@ -25,21 +25,25 @@ #' in the second if table = TRUE. #' #' @examples -#' library(Lock5Data) -#' dataz <- data(CommuteAtlanta) #' -#' func = function(data, index) { -#' x <- as.numeric(unlist(data[1])) -#' y <- as.numeric(unlist(data[2])) -#' return(mean(x[index]) - mean(y[index])) +#' \donttest{ +#' data(diabetes, package = "bcaboot") +#' Xy <- cbind(diabetes$x, diabetes$y) +#' rfun <- function(Xy) { +#' y <- Xy[, 11] +#' X <- Xy[, 1:10] +#' return(summary(lm(y ~ X))$adj.r.squared) #' } #' -#' z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) -#' ggconcurve(data = z[[1]]) -#' ggconcurve(data = z[[2]], type = "cd") +#' x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000) #' - -curve_boot <- function(data = data, func = func, method = "bca", replicates = 20000, steps = 1000, table = TRUE) { +#' ggconcurve(data = x[[1]]) +#' ggconcurve(data = x[[3]]) +#' +#' plot_compare(x[[1]], x[[3]]) +#' } +#' +curve_boot <- function(data = data, func = func, method = "bca", replicates = 2000, steps = 1000, table = TRUE) { # BCA Bootstrap Method --------------------------------------------------- @@ -59,14 +63,14 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 # Data Frame with BCA Intervals ------------------------------ - bca <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), mc.cores = detectCores() - 1) + bca <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$bca, i), nth(z$bca, -i)), mc.cores = getOption("mc.cores", 2L)) bcaintervals <- data.frame(do.call(rbind, bca)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(bcaintervals) <- intrvl.limit - news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), mc.cores = detectCores() - 1) + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$bca, -i) - nth(z$bca, i), mc.cores = getOption("mc.cores", 2L)) width <- data.frame(do.call(rbind, news)) colnames(width) <- "intrvl.width" - bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = detectCores() - 1) + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = getOption("mc.cores", 2L)) levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" @@ -82,14 +86,14 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 # Data Frame with Standard Intervals ------------------------------ - std <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), mc.cores = detectCores() - 1) + std <- pbmclapply(1:length(alpha), FUN = function(i) c(nth(z$std, i), nth(z$std, -i)), mc.cores = getOption("mc.cores", 2L)) stdintervals <- data.frame(do.call(rbind, std)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(stdintervals) <- intrvl.limit - news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), mc.cores = detectCores() - 1) + news <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$std, -i) - nth(z$std, i), mc.cores = getOption("mc.cores", 2L)) width <- data.frame(do.call(rbind, news)) colnames(width) <- "intrvl.width" - bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = detectCores() - 1) + bews <- pbmclapply(1:length(alpha), FUN = function(i) nth(z$alphaperc, -i) - nth(z$alphaperc, i), mc.cores = getOption("mc.cores", 2L)) levels <- data.frame(do.call(rbind, bews)) colnames(levels) <- "intrvl.level" @@ -126,13 +130,13 @@ curve_boot <- function(data = data, func = func, method = "bca", replicates = 20 # Boot Percentile Method For Density -------------------------------------- } else if (method == "t") { - t.boot <- boot(data = data, statistic = func, R = replicates, parallel = "multicore", ncpus = (detectCores())) + t.boot <- boot(data = data, statistic = func, R = replicates, parallel = "multicore", ncpus = getOption("mc.cores", 2L)) intrvls <- 1:steps / steps t <- pbmclapply(intrvls, FUN = function(i) boot.ci(t.boot, conf = i, type = "perc")$perc[4:5], - mc.cores = detectCores() + mc.cores = getOption("mc.cores", 2L) ) df <- data.frame(do.call(rbind, t)) diff --git a/R/curve_compare.R b/R/curve_compare.R index d13c474..2711070 100644 --- a/R/curve_compare.R +++ b/R/curve_compare.R @@ -17,20 +17,21 @@ #' @param ... Can be used to pass further arguments to plot_compare(). #' #' @examples +#' \donttest{ #' library(concurve) #' GroupA <- rnorm(500) #' GroupB <- rnorm(500) #' RandomData <- data.frame(GroupA, GroupB) -#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) #' GroupA2 <- rnorm(500) #' GroupB2 <- rnorm(500) #' RandomData2 <- data.frame(GroupA2, GroupB2) #' model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) -#' randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) -#' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) -#' (curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) +#' randomframe <- curve_gen(model, "GroupB2", method = "glm") +#' (curve_compare(intervalsdf[[1]], randomframe[[1]])) +#' (curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) +#' } #' - curve_compare <- function(data1, data2, type = "c", plot = TRUE, ...) { # Consonance Function ----------------------------------------------------- diff --git a/R/curve_corr.R b/R/curve_corr.R index 6ee3e22..5502b98 100644 --- a/R/curve_corr.R +++ b/R/curve_corr.R @@ -25,16 +25,12 @@ #' statistics should be generated. The default is TRUE and generates a table #' which is included in the list object. #' -#' @return -#' @export -#' #' @examples +#' #' GroupA <- rnorm(50) #' GroupB <- rnorm(50) #' joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") #' tibble::tibble(joe[[1]]) -#' - curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { if (is.numeric(x) != TRUE) { stop("Error: 'x' must be a numeric vector") @@ -53,7 +49,7 @@ curve_corr <- function(x, y, alternative, method, steps = 10000, table = TRUE) { alternative = alternative, method = method, exact = NULL, conf.level = i, continuity = FALSE )$conf.int[] - }, mc.cores = detectCores() - 1) + }, mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_gen.R b/R/curve_gen.R index ac601c9..bd2db6f 100644 --- a/R/curve_gen.R +++ b/R/curve_gen.R @@ -30,10 +30,9 @@ #' statistics should be generated. The default is TRUE and generates a table #' which is included in the list object. #' -#' @return -#' @export -#' #' @examples +#' +#' \donttest{ #' # Simulate random data #' GroupA <- rnorm(50) #' GroupB <- rnorm(50) @@ -41,7 +40,8 @@ #' rob <- glm(GroupA ~ GroupB, data = RandomData) #' bob <- curve_gen(rob, "GroupB", method = "glm") #' tibble::tibble(bob[[1]]) - +#' } +#' curve_gen <- function(model, var, method = "wald", steps = 1000, table = TRUE) { if (is.character(method) != TRUE) { stop("Error: 'method' must be a character vector") @@ -53,9 +53,9 @@ curve_gen <- function(model, var, method = "wald", steps = 1000, table = TRUE) { intrvls <- (1:(steps - 1)) / steps if (method == "wald") { - results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = getOption("mc.cores", 2L)) } else if (method == "glm") { - results <- pbmclapply(intrvls, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ], mc.cores = getOption("mc.cores", 2L)) } df <- data.frame(do.call(rbind, results)) diff --git a/R/curve_lik.R b/R/curve_lik.R index fd70a9a..49a3f80 100644 --- a/R/curve_lik.R +++ b/R/curve_lik.R @@ -8,12 +8,12 @@ #' which is included in the list object. #' #' @examples +#' #' library(ProfileLikelihood) #' data(dataglm) -#' xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) +#' xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit")) #' lik <- curve_lik(xx, dataglm) #' tibble::tibble(lik[[1]]) - curve_lik <- function(likobject, data, table = TRUE) { values <- likobject[[1]] # theta values likelihood <- likobject[[2]] # profile likelihoods diff --git a/R/curve_mean.R b/R/curve_mean.R index b91e620..682edb3 100644 --- a/R/curve_mean.R +++ b/R/curve_mean.R @@ -54,7 +54,7 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = intrvls <- (0:steps) / steps if (method == "default") { - results <- pbmclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) t.test(x, y, data = data, paired = paired, conf.level = i)$conf.int[], mc.cores = getOption("mc.cores", 2L)) } else if (method == "boot") { diff <- mean(x) - mean(y) if (paired) { @@ -68,7 +68,7 @@ curve_mean <- function(x, y, data, paired = F, method = "default", replicates = mean(sample(y, length(y), replace = T)) ) - diff } - results <- pbmclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) diff - quantile(boot_dist, probs = (1 + c(i, -i)) / 2), mc.cores = getOption("mc.cores", 2L)) } df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") diff --git a/R/curve_meta.R b/R/curve_meta.R index 864e7b2..a1434d1 100644 --- a/R/curve_meta.R +++ b/R/curve_meta.R @@ -24,7 +24,7 @@ #' #' @examples #' -#' # Simulate random data for two groups in two studies +#' # Simulate random data for two groups in two studies #' GroupAData <- runif(20, min = 0, max = 100) #' GroupAMean <- round(mean(GroupAData), digits = 2) #' GroupASD <- round(sd(GroupAData), digits = 2) @@ -80,7 +80,7 @@ curve_meta <- function(x, measure = "default", steps = 10000, table = TRUE) { } intrvls <- (0:steps) / steps - results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) confint.default(object = x, fixed = TRUE, random = FALSE, level = i)[], mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, results)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_rev.R b/R/curve_rev.R index ae4bb22..2a8c9ca 100644 --- a/R/curve_rev.R +++ b/R/curve_rev.R @@ -56,8 +56,8 @@ curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10 if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) - UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = getOption("mc.cores", 2L)) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -66,8 +66,8 @@ curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10 else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) - logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = getOption("mc.cores", 2L)) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -106,8 +106,8 @@ curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10 if (measure == "default") { se <- (UL / LL) / 3.92 - LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = detectCores() - 1) - UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = detectCores() - 1) + LL <- pbmclapply(z, FUN = function(i) point + (i * se), mc.cores = getOption("mc.cores", 2L)) + UL <- pbmclapply(z, FUN = function(i) point - (i * se), mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, UL), do.call(rbind, LL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit @@ -116,8 +116,8 @@ curve_rev <- function(point, LL, UL, type = "c", measure = "default", steps = 10 else if (measure == "ratio") { se <- log(UL / LL) / 3.92 logpoint <- log(point) - logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = detectCores() - 1) - logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = detectCores() - 1) + logLL <- pbmclapply(z, FUN = function(i) logpoint + (i * se), mc.cores = getOption("mc.cores", 2L)) + logUL <- pbmclapply(z, FUN = function(i) logpoint - (i * se), mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, logUL), do.call(rbind, logLL)) intrvl.limit <- c("lower.limit", "upper.limit") colnames(df) <- intrvl.limit diff --git a/R/curve_surv.R b/R/curve_surv.R index cdd6013..1c90049 100644 --- a/R/curve_surv.R +++ b/R/curve_surv.R @@ -18,8 +18,8 @@ #' @param table Indicates whether or not a table output with some relevant #' statistics should be generated. The default is TRUE and generates a table #' which is included in the list object. -#' @examples #' + curve_surv <- function(data, x, steps = 10000, table = TRUE) { if (is.list(data) != TRUE) { stop("Error: 'data' must be an object with a Cox Proportional Hazards model") @@ -29,7 +29,7 @@ curve_surv <- function(data, x, steps = 10000, table = TRUE) { } intrvls <- (1:steps) / steps - results <- pbmclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], mc.cores = detectCores() - 1) + results <- pbmclapply(intrvls, FUN = function(i) summary(data, conf.int = i)$conf.int[x, ], mc.cores = getOption("mc.cores", 2L)) df <- data.frame(do.call(rbind, results))[, 3:4] intrvl.limit <- c("lower.limit", "upper.limit") diff --git a/R/curve_table.R b/R/curve_table.R index 0488dc1..e694d81 100644 --- a/R/curve_table.R +++ b/R/curve_table.R @@ -27,13 +27,11 @@ #' (z <- curve_table(intervalsdf[[1]], format = "data.frame")) #' (z <- curve_table(intervalsdf[[1]], format = "tibble")) #' (z <- curve_table(intervalsdf[[1]], format = "latex")) - - curve_table <- function(data, levels, type = "c", format = "data.frame") { if (type == "c") { levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99) - subdf <- pbmclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = detectCores() - 1) + subdf <- pbmclapply(levels, FUN = function(i) (subset(data, intrvl.level == i)), mc.cores = getOption("mc.cores", 2L)) subdf <- data.frame(do.call(rbind, subdf)) class(subdf) <- c("data.frame", "concurve") subdf$intrvl.level <- (subdf$intrvl.level * 100) @@ -43,7 +41,7 @@ curve_table <- function(data, levels, type = "c", format = "data.frame") { } else if (type == "l") { levels <- c(0.03, 0.05, 0.12, 0.14) - subdf <- pbmclapply(levels, FUN = function(i) (subset(data, round(support, 2) == i)), mc.cores = detectCores() - 1) + subdf <- pbmclapply(levels, FUN = function(i) (subset(data, round(support, 2) == i)), mc.cores = getOption("mc.cores", 2L)) subdf <- data.frame(do.call(rbind, subdf)) class(subdf) <- c("data.frame", "concurve") subcolnames <- c("Theta", "Likelihood", "Log Likelihood", "Relative Likelihood", "Deviance Statistic") diff --git a/R/defunct.R b/R/defunct.R index 24cc3bb..4b613da 100644 --- a/R/defunct.R +++ b/R/defunct.R @@ -5,8 +5,8 @@ defunct <- function(msg = "This function is deprecated") { } # Graphical functions -plotpint <- defunct("plotpint() is now deprecated. Please use ggconcurve() or plot_concurve() instead.") -plotsint <- defunct("plotsint() is now deprecated. Please use ggconcurve() or plot_concurve() instead.") +plotpint <- defunct("plotpint() is now deprecated. Please use ggconcurve() instead.") +plotsint <- defunct("plotsint() is now deprecated. Please use ggconcurve() instead.") # Computational functions meanintervals <- defunct("meanintervals() is now deprecated. Please use curve_mean() instead.") diff --git a/R/ggconcurve.R b/R/ggconcurve.R index 4c2c7a5..df818f7 100644 --- a/R/ggconcurve.R +++ b/R/ggconcurve.R @@ -30,7 +30,7 @@ #' plotted as a vertical line. Changing this to TRUE, will plot a vertical #' line at 0 when the measure is set to " default" and a vertical line at #' 1 when the measure is set to "ratio". For example, -#' ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +#' ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). #' This feature is not yet available for surprisal functions. #' @param position Determines the orientation of the P-value (consonance) function. #' By default, it is set to "pyramid", meaning the p-value function will @@ -40,32 +40,33 @@ #' ggconcurve(type = "c", data = df, position = "inverted"). #' @param title A custom title for the graph. By default, it is #' set to "Consonance Function". In order to set a title, it must -#' be in quotes. For example, ggconcurve(type = "consonance", +#' be in quotes. For example, ggconcurve(type = "c", #' data = x, title = "Custom Title"). #' @param subtitle A custom subtitle for the graph. By default, it is set #' to "The function contains consonance/confidence intervals at every level #' and the P-values." In order to set a subtitle, it must be in quotes. -#' For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle"). +#' For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle"). #' @param xaxis A custom x-axis title for the graph. By default, #' it is set to "Range of Values. #' In order to set a x-axis title, it must be in quotes. For example, -#' ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio"). +#' ggconcurve(type = "c", data = x, xaxis = "Hazard Ratio"). #' @param yaxis A custom y-axis title for the graph. By default, #' it is set to "Consonance Level". #' In order to set a y-axis title, it must be in quotes. For example, -#' ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). +#' ggconcurve(type = "c", data = x, yxis = "Confidence Level"). #' @param color Item that allows the user to choose the color of the points #' and the ribbons in the graph. By default, it is set to color = "#555555". #' The inputs must be in quotes. -#' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). +#' For example, ggconcurve(type = "c", data = x, color = "#333333"). #' @param fill Item that allows the user to choose the color of the ribbons in the graph. -#' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +#' By default, it is set to fill = "#239a98". The inputs must be in quotes. For example, #' ggconcurve(type = "c", data = x, fill = "#333333"). #' #' @return Plot with intervals at every consonance level graphed with their corresponding #' p-values and compatibility levels. #' #' @examples +#' #' # Simulate random data #' #' library(concurve) @@ -77,7 +78,6 @@ #' #' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") #' (function1 <- ggconcurve(type = "c", intervalsdf[[1]])) - ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nullvalue = FALSE, position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", @@ -122,7 +122,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores() - 1) + interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = getOption("mc.cores", 2L)) interval <- data.frame(do.call(rbind, interval)) interval <- pivot_longer(interval, X2:X3, names_to = "levels", values_to = "limits") interval <- interval[, -2] @@ -226,7 +226,7 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # Plotting Intervals ------------------------------------------------------ - interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = detectCores() - 1) + interval <- pbmclapply(levels, FUN = function(i) (c(i, subset(data, intrvl.level == i)[, 1], subset(data, intrvl.level == i)[, 2])), mc.cores = getOption("mc.cores", 2L)) interval <- data.frame(do.call(rbind, interval)) interval <- gather(interval, key = "levels", value = "limits", X2:X3) interval <- interval[, -2] @@ -622,3 +622,4 @@ ggconcurve <- function(data, type = "c", measure = "default", levels = 0.95, nul # RMD Check utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")) +utils::globalVariables(c("X2", "X3", "limits", "x")) diff --git a/R/plot_compare.R b/R/plot_compare.R index de35204..7a22543 100644 --- a/R/plot_compare.R +++ b/R/plot_compare.R @@ -24,7 +24,7 @@ #' plotted as a vertical line. Changing this to TRUE, will plot a vertical #' line at 0 when the measure is set to " default" and a vertical line at #' 1 when the measure is set to "ratio". For example, -#' ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +#' ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). #' This feature is not yet available for surprisal functions. #' @param position Determines the orientation of the P-value (consonance) function. #' By default, it is set to "pyramid", meaning the p-value function will @@ -34,12 +34,12 @@ #' ggconcurve(type = "c", data = df, position = "inverted"). #' @param title A custom title for the graph. By default, it is #' set to "Consonance Function". In order to set a title, it must -#' be in quotes. For example, ggconcurve(type = "consonance", +#' be in quotes. For example, ggconcurve(type = "c", #' data = x, title = "Custom Title"). #' @param subtitle A custom subtitle for the graph. By default, it is set #' to "The function contains consonance/confidence intervals at every level #' and the P-values." In order to set a subtitle, it must be in quotes. -#' For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle"). +#' For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle"). #' @param xaxis A custom x-axis title for the graph. By default, #' it is set to "Range of Values. #' In order to set a x-axis title, it must be in quotes. For example, @@ -47,23 +47,26 @@ #' @param yaxis A custom y-axis title for the graph. By default, #' it is set to "Consonance Level". #' In order to set a y-axis title, it must be in quotes. For example, -#' ggconcurve(type = "consonance", data = x, yxis = "Confidence Level"). +#' ggconcurve(type = "c", data = x, yxis = "Confidence Level"). #' @param color Item that allows the user to choose the color of the points #' and the ribbons in the graph. By default, it is set to color = "#555555". #' The inputs must be in quotes. -#' For example, ggconcurve(type = "consonance", data = x, color = "#333333"). -#' @param fill Item that allows the user to choose the color of the ribbons in the graph. -#' By default, it is set to color = "#239a98". The inputs must be in quotes. For example, -#' ggconcurve(type = "consonance", data = x, fill = "#333333"). -#' +#' For example, ggconcurve(type = "c", data = x, color = "#333333"). +#' @param fill1 Item that allows the user to choose the color of the ribbons in the graph +#' for data1. By default, it is set to fill1 = "#239a98". The inputs must be in quotes. +#' For example, ggconcurve(type = "c", data = x, fill1 = "#333333"). +#' @param fill2 Item that allows the user to choose the color of the ribbons in the graph +#' for data1. By default, it is set to fill2 = "#d46c5b". The inputs must be in quotes. +#' For example, ggconcurve(type = "c", data = x, fill2 = "#333333"). #' @return A plot that compares two functions. #' @examples +#' \donttest{ #' library(concurve) #' #' GroupA <- rnorm(500) #' GroupB <- rnorm(500) #' RandomData <- data.frame(GroupA, GroupB) -#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +#' intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) #' GroupA2 <- rnorm(500) #' GroupB2 <- rnorm(500) #' RandomData2 <- data.frame(GroupA2, GroupB2) @@ -71,8 +74,9 @@ #' #' randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) #' -#' (plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = FALSE)) - +#' (plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) +#' } +#' plot_compare <- function(data1, data2, type = "c", measure = "default", nullvalue = FALSE, position = "pyramid", title = "Interval Functions", subtitle = "The function displays intervals at every level.", diff --git a/R/utils-pipe.R b/R/utils-pipe.R deleted file mode 100644 index e79f3d8..0000000 --- a/R/utils-pipe.R +++ /dev/null @@ -1,11 +0,0 @@ -#' Pipe operator -#' -#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. -#' -#' @name %>% -#' @rdname pipe -#' @keywords internal -#' @export -#' @importFrom magrittr %>% -#' @usage lhs \%>\% rhs -NULL diff --git a/codemeta.json b/codemeta.json index d7be97b..42247b6 100644 --- a/codemeta.json +++ b/codemeta.json @@ -320,18 +320,6 @@ }, "sameAs": "https://CRAN.R-project.org/package=tidyr" }, - { - "@type": "SoftwareApplication", - "identifier": "magrittr", - "name": "magrittr", - "provider": { - "@id": "https://cran.r-project.org", - "@type": "Organization", - "name": "Comprehensive R Archive Network (CRAN)", - "url": "https://cran.r-project.org" - }, - "sameAs": "https://CRAN.R-project.org/package=magrittr" - }, { "@type": "SoftwareApplication", "identifier": "MASS", @@ -344,6 +332,11 @@ }, "sameAs": "https://CRAN.R-project.org/package=MASS" }, + { + "@type": "SoftwareApplication", + "identifier": "methods", + "name": "methods" + }, { "@type": "SoftwareApplication", "identifier": "R", @@ -352,7 +345,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3874.265KB", + "fileSize": "3837.019KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/data/CRANpackages.rda b/data/CRANpackages.rda deleted file mode 100644 index 0e29a6a8cf47320028e53fc6b977c5c6f593fc5c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 819 zcmV-31I+wFT4*^jL0KkKS;4LW5C8+FfB*mg|JPMjfByZ|T}=P)|KMcBg`rI*$^b$C zhyV-#Kq0^arws#5TMZPaspvohLqjHj27vWFO*9&9Pe33V02*ikpbY@f8X2je(h_=8 z^+$+LQJ`oV01wpwGynhq00000007Ve4445h35k;?00h9835kMW00004$iPepVE`mi zG)bnBni)eA5M%%V8feo@fB@4*fB*mh9-|?qnrXTS2FgMe3ZRYqXv`8;b(H0W#D{U& zON&Rz8N~NBCUf5W*$`RXf|+R6^{RF1VL8KQ>k}6n9o|F5a$GF+^Y%ATE0Z7siUKGG z0h^ZSk%rlYi9$#xFy|CR1dK@(l`IHQQ9=Q@#l(t66p0Zy3|MV&SYzwpgGBKj?}u&v zcHvSptqK??jN-I!ABzI6-;Gj>LJG126RbATlSLW>ogU;va@#NfoUSc#aA=H>|`9j94=A%NQQ z$_XMMSwTgk*$nTjCP(k3?waVWix9D!0<*rVmE&!;+ikxuzDE?5a2r+<~1dr^o}&Ivbm*v8n{#*=J*ZBBkKc^Y!lCDMfZ-eiAX zIZOuQ#KYUAlL{&@c`SoS%toRfp6mn588R@nE#6YMm4QEY@L+rYYDrtDAe0 zER!JWy^RH%VAvJSK9cs(Evns+E0d-Q6|iQH-Opg8ekf*^oihM^VH!_x+a9A7LV&>` z(s+Z0z$Tagnlpi-ID{OQOWzRCOE9h_Ze^OA1hVaJ6!$sRK+%I0xr3KvU`}Lb!lTnL z81Ka7fwvoFlikX)fFytc_?bl=qXGnD#6&P+ZYS-&U-#U4-%XOyX6vsj`Wnp1;k}B4 xqqjPz=m3%BoYRP8O^gr@3=p3_KffnHr_(~&AilVSC?@}lxgwk>NMP3hhyaqKZY=-+ diff --git a/data/Depression_Ginzberg.rda b/data/Depression_Ginzberg.rda deleted file mode 100644 index 61400555268958fee114e6cb68ff1085ec5ebfd1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3700 zcmV-)4vX*nr3-SzJGweH=n7Wk^EaMX#U(lTVxpwQ3;hCpa( zriRiq!Uw1T13(&R(dizisPbshGHB3gpazXJ2cl?V9#HhrqyTz=&q)Bp+A^8}>TOR! 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    This outlines how to propose a change to concurve. For more detailed info about contributing to this, and other tidyverse packages, please see the development contributing guide.

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    +Fixing typos

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    Small typos or grammatical errors in documentation may be edited directly using the GitHub web interface, so long as the changes are made in the source file.

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    • YES: you edit a roxygen comment in a .R file below R/.
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    • NO: you edit an .Rd file below man/.
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    +Prerequisites

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    Before you make a substantial pull request, you should always file an issue and make sure someone from the team agrees that it’s a problem. If you’ve found a bug, create an associated issue and illustrate the bug with a minimal reprex.

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    +Pull request process

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    • We recommend that you create a Git branch for each pull request (PR).
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    • We use roxygen2, with Markdown syntax, for documentation.
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    +Code of Conduct

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    Please note that the concurve project is released with a Contributor Code of Conduct. By contributing to this project you agree to abide by its terms.

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    +See tidyverse development contributing guide +

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    for further details.

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    Please briefly describe your problem and what output you expect. If you have a question, please don’t use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.

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    Please include a minimal reproducible example (AKA a reprex). If you’ve never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

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    Brief description of the problem

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    Version 3, 29 June 2007
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    +
    +
    +
    +

    +How to Apply These Terms to Your New Programs

    +

    If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

    +

    To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

    + +

    Also add information on how to contact you by electronic and paper mail.

    +

    If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

    + +

    The hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.

    +

    You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.

    +

    The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.

    +
    +
    + +
    + +
    + + + + +
    + + + + + + + + + + + diff --git a/docs/SUPPORT.html b/docs/SUPPORT.html new file mode 100644 index 0000000..12312f0 --- /dev/null +++ b/docs/SUPPORT.html @@ -0,0 +1,208 @@ + + + + + + + + +Getting help with concurve • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    + +

    Thanks for using concurve. Before filing an issue, there are a few places to explore and pieces to put together to make the process as smooth as possible.

    +

    Start by making a minimal reproducible example using the reprex package. If you haven’t heard of or used reprex before, you’re in for a treat! Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it’ll take you to learn what it’s all about). For additional reprex pointers, check out the Get help! section of the tidyverse site.

    +

    Armed with your reprex, the next step is to figure out where to ask.

    +
      +
    • If it’s a question: start with community.rstudio.com, and/or StackOverflow. There are more people there to answer questions.
      +
    • +
    • If it’s a bug: you’re in the right place, file an issue.
      +
    • +
    • If you’re not sure: let the community help you figure it out! If your problem is a bug or a feature request, you can easily return here and report it.
    • +
    +

    Before opening a new issue, be sure to search issues and pull requests to make sure the bug hasn’t been reported and/or already fixed in the development version. By default, the search will be pre-populated with is:issue is:open. You can edit the qualifiers (e.g. is:pr, is:closed) as needed. For example, you’d simply remove is:open to search all issues in the repo, open or closed.

    +

    If you are in the right place, and need to file an issue, please review the “File issues” paragraph from the tidyverse contributing guidelines.

    +

    Thanks for your help!

    +
    + +
    + +
    + + + + +
    + + + + + + + + + + + diff --git a/docs/articles/examples.html b/docs/articles/examples.html new file mode 100644 index 0000000..ca289e2 --- /dev/null +++ b/docs/articles/examples.html @@ -0,0 +1,324 @@ + + + + + + + +Examples in R • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    + + + + +
    +

    +Using Mean Differences

    +

    If we were to generate some random data from a normal distribution with the following code,

    + +

    and compare the means of these two “groups” using a t-test,

    +
    testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE)
    +

    we would likely see some differences, given that we have such a small sample in each group.

    +
    summary(testresults)
    +

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

    +

    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

    +

    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

    +

    It is relatively easy to produce such a function using the concurve package in R.

    +

    Install the package directly from CRAN

    +
    install.packages("concurve")
    +

    or get a more slightly up-to-date version via GitHub.

    +
    library(devtools)
    +install_github("zadchow/concurve")
    +

    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    + +

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    +
    tibble::tibble(intervalsdf)
    +

    There’s a quick look at the first few entries of our dataframe.

    +

    We can plot this data using the ggconcurve() function.

    +
    pfunction <- ggconcurve(type = "consonance", intervalsdf)
    +pfunction
    +
    + +
    +

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    +

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    +
    sfunction <- ggconcurve(type = "surprisal", intervalsdf)
    +sfunction
    +
    + +
    +

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    +
    +
    +

    +Simple Linear Models

    +

    We can also try this with other simple linear models.

    +

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    +
    GroupA2 <- rnorm(500)
    +GroupB2 <- rnorm(500)
    +
    +RandomData2 <- data.frame(GroupA2, GroupB2)
    +
    +model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    +
    +summary(model)
    +

    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    +
    randomframe <- curve_gen(model, "GroupB2")
    +tibble::tibble(randomframe)
    +

    Now that we have our data frame, we can graph our function.

    +
    ggconcurve(type = "consonance", randomframe)
    +
    + +
    +
    s <- ggconcurve(type = "surprisal", randomframe)
    +s
    +
    + +
    +

    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    + +

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    + +
    + +
    +

    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    +
    +
    +

    +Using Meta-Analysis Data

    +

    Here we present another quick example with a meta-analysis of simulated data.

    +

    First, we generate random data for two groups in two hypothetical studies

    +
    GroupAData <- runif(20, min = 0, max = 100)
    +GroupAMean <- round(mean(GroupAData), digits = 2)
    +GroupASD <- round(sd(GroupAData), digits = 2)
    +
    +GroupBData <- runif(20, min = 0, max = 100)
    +GroupBMean <- round(mean(GroupBData), digits = 2)
    +GroupBSD <- round(sd(GroupBData), digits = 2)
    +
    +GroupCData <- runif(20, min = 0, max = 100)
    +GroupCMean <- round(mean(GroupCData), digits = 2)
    +GroupCSD <- round(sd(GroupCData), digits = 2)
    +
    +GroupDData <- runif(20, min = 0, max = 100)
    +GroupDMean <- round(mean(GroupDData), digits = 2)
    +GroupDSD <- round(sd(GroupDData), digits = 2)
    +

    We can then quickly combine the data in a dataframe.

    +
    StudyName <- c("Study1", "Study2")
    +MeanTreatment <- c(GroupAMean, GroupCMean)
    +MeanControl <- c(GroupBMean, GroupDMean)
    +SDTreatment <- c(GroupASD, GroupCSD)
    +SDControl <- c(GroupBSD, GroupDSD)
    +NTreatment <- c(20, 20)
    +NControl <- c(20, 20)
    +
    +metadf <- data.frame(
    +  StudyName, MeanTreatment, MeanControl,
    +  SDTreatment, SDControl,
    +  NTreatment, NControl
    +)
    +

    Then, we’ll use metafor to calculate the standardized mean difference.

    + +

    Next, we’ll pool the data using a fixed-effects common-effects model

    + +

    Let’s look at our output.

    + +
    Fixed-Effects Model (k = 2)
    +
    +Test for Heterogeneity: 
    +Q(df = 1) = 0.61, p-val = 0.44
    +
    +Model Results:
    +
    +estimate    se  zval  pval  ci.lb  ci.ub 
    +    0.16  0.22  0.73  0.47  -0.28   0.60  
    +
    +---
    +Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    +

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    +

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    + +

    Now that we have our dataframe with every computed interval, we can plot the functions.

    +
    ggconcurve(type = "consonance", metaf)
    +
    + +
    +

    And our S-value function

    +
    ggconcurve(type = "surprisal", metaf)
    +
    + +
    +

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

    +
    +
    + + + +
    + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + +

    + + + + + + + + + + + diff --git a/docs/articles/stata.html b/docs/articles/stata.html new file mode 100644 index 0000000..37c5ef4 --- /dev/null +++ b/docs/articles/stata.html @@ -0,0 +1,302 @@ + + + + + + + +Using Stata • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    + + + + +

    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

    +

    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

    + +

    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

    +

    First, let’s visualize the data with a scatterplot.

    + +
    + +
    +


    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

    +

    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

    + +
    + +
    +


    That’s what our output looks like.

    +

    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

    +
    + +
    +


    +

    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

    +

    Here’s the code that we’ll be using to achieve that.

    + +

    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

    + +

    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

    +
      +
    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

    • +
    • pvalue”is computed by taking 1 - “level”, which is alpha.

    • +
    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

    • +
    • my_new_data” is the filename that we’ve assigned to our new dataset.

    • +
    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

    • +
    +

    Here are the next few major lines

    + +

    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

    +

    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

    +

    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

    +

    The next line two lines

    + +

    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

    + +

    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

    +
    + +
    +


    +

    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

    +

    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

    +

    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

    +

    “post topost” is writing the results from each loop as new observations in this data structure.

    +

    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

    + +

    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

    + +

    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

    +

    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

    +

    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

    +

    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

    +

    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

    + +

    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

    +
    + +
    +


    +

    So now, here’s what our consonance function looks like.

    +
    + +
    +


    And here’s what our surprisal function looks like.

    +
    + +
    +


    It looks slighly better when the y-axis is transformed logarithmically…

    +
    + +
    +


    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

    +

    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

    +

    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

    +

    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

    + +

    We simply replace the first line within the loop with our intended command, just as I’ve replaced

    + +

    with

    + +

    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

    + +

    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

    +
    + + + +
    + + + + +
    + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 0000000..660c3b9 --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,218 @@ + + + + + + + + +Citation and Authors • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +

    Chow Z, Vigotsky A (2019). +concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. +https://CRAN.R-project.org/package=concurve. +

    +
    @Manual{,
    +  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
    +  author = {Zad R. Chow and Andrew D. Vigotsky},
    +  year = {2019},
    +  url = {https://CRAN.R-project.org/package=concurve},
    +}
    + + + + + +
    + +
    + + + + +
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+ padding: .1rem 1rem; + margin-bottom: 0.1; + font-size: 1.0em; + font-weight: 400 + /* display: none */ +} + +.algolia-autocomplete .algolia-docsearch-suggestion--title { + display: block; + padding: .25rem 1rem; + margin-bottom: 0; + font-size: 0.9em; + font-weight: 400 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--text { + padding: 0 1rem .5rem; + margin-top: -.25rem; + font-size: 0.8em; + font-weight: 400; + line-height: 1.25 +} + +.algolia-autocomplete .algolia-docsearch-footer { + width: 110px; + height: 20px; + z-index: 3; + margin-top: 10.66667px; + float: right; + font-size: 0; + line-height: 0; +} + +.algolia-autocomplete .algolia-docsearch-footer--logo { + background-image: url("data:image/svg+xml;utf8,"); + background-repeat: no-repeat; + background-position: 50%; + background-size: 100%; + overflow: hidden; + text-indent: -9000px; + width: 100%; + height: 100%; + display: block; + transform: translate(-8px); +} + +.algolia-autocomplete .algolia-docsearch-suggestion--highlight { + color: #FF8C00; + background: rgba(232, 189, 54, 0.1) +} + + +.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { + box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) +} + +.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { + background-color: rgba(192, 192, 192, .15) +} diff --git a/docs/docsearch.js b/docs/docsearch.js new file mode 100644 index 0000000..b35504c --- /dev/null +++ b/docs/docsearch.js @@ -0,0 +1,85 @@ +$(function() { + + // register a handler to move the focus to the search bar + // upon pressing shift + "/" (i.e. "?") + $(document).on('keydown', function(e) { + if (e.shiftKey && e.keyCode == 191) { + e.preventDefault(); + $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/docsearch.json b/docs/docsearch.json new file mode 100644 index 0000000..aa1fc65 --- /dev/null +++ b/docs/docsearch.json @@ -0,0 +1,95 @@ +{ + "index_name": "lesslikely-concurve", + "start_urls": [ + { + "url": "https://data.lesslikely.com/concurve//index.html", + "selectors_key": "homepage", + "tags": [ + "homepage" + ] + }, + { + "url": "https://data.lesslikely.com/concurve//reference", + "selectors_key": "reference", + "tags": [ + "reference" + ] + }, + { + "url": "https://data.lesslikely.com/concurve//articles", + "selectors_key": "articles", + "tags": [ + "articles" + ] + } + ], + "stop_urls": [ + "/reference/$", + "/reference/index.html", + "/articles/$", + "/articles/index.html" + ], + "sitemap_urls": [ + "https://data.lesslikely.com/concurve//sitemap.xml" + ], + "selectors": { + "homepage": { + "lvl0": { + "selector": ".contents h1", + "default_value": "concurve Home page" + }, + "lvl1": { + "selector": ".contents h2" + }, + "lvl2": { + "selector": ".contents h3", + "default_value": "Context" + }, + "lvl3": ".ref-arguments td, .ref-description", + "text": ".contents p, .contents li, .contents .pre" + }, + "reference": { + "lvl0": { + "selector": ".contents h1" + }, + "lvl1": { + "selector": ".contents .name", + "default_value": "Argument" + }, + "lvl2": { + "selector": ".ref-arguments th", + "default_value": "Description" + }, + "lvl3": ".ref-arguments td, .ref-description", + "text": ".contents p, .contents li" + }, + "articles": { + "lvl0": { + "selector": ".contents h1" + }, + "lvl1": { + "selector": ".contents .name" + }, + "lvl2": { + "selector": ".contents h2, .contents h3", + "default_value": "Context" + }, + "text": ".contents p, .contents li, .tempate-article .contents .pre" + } + }, + "selectors_exclude": [ + ".dont-index" + ], + "min_indexed_level": 2, + "custom_settings": { + "separatorsToIndex": "_", + "attributesToRetrieve": [ + "hierarchy", + "content", + "anchor", + "url", + "url_without_anchor" + ] + } +} + diff --git a/docs/extra.css b/docs/extra.css new file mode 100644 index 0000000..0f1be09 --- /dev/null +++ b/docs/extra.css @@ -0,0 +1,98 @@ +html, body { + height: 100%; + font-size: 18px; + font-family: "PT Serif"; +} + +.navbar-brand { + font-size: 16px; + font-weight: bold; +} + +.navbar-default .navbar-nav>li>a:hover, +.navbar-default .navbar-nav>li>a:focus { + background-color: #eee; +} + +.nav-pills>li.active>a, +.nav-pills>li.active>a:hover, +.nav-pills>li.active>a:focus { + background-color:#d46c5b; +} + +.label-default { + background-color:#d46c5b; +} + +p.caption { + display: none; +} + +body { + color: #444444; + line-height: 1.6; +} + +a { + color: #d46c5b; +} + +a:focus { + color: #d46c5b; +} + +h1, .h1 { + font-size: 32px; +} + +h1 small, .h1 small { + font-size: 70%; + text-transform: uppercase; + letter-spacing: 0.5px; +} + +h2, .h2 { + font-size: 28px; +} + +h3, .h3 { + font-size: 20px; +} + +h4, .h4 { + font-size: 20px; +} + +.contents h1, .contents h2, .contents h3, .contents h4 { + margin-bottom: 20px; +} + +.contents .page-header h1 { + margin-top: 25px; +} + +.contents h3, .contents h4 { + margin-top: -55px; +} + +h4.date, h4.author { + font-size: 1em; + color: #bbb; + display: inline; + padding-right: 0.5em; +} + +pre code { + font-size: 13.5px; + white-space: pre; +} + +.btn-primary { + color: #eeeeee; + background-color: #d46c5b; +} + +.btn-primary:focus { + color: #eeeeee; + background-color: #d46c5b; +} diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..b8d7886 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,365 @@ + + + + + + + +Computes and Plots Compatibility (Confidence) Intervals, + P-Values, S-Values, & Likelihood Intervals to Form Consonance, + Surprisal, & Likelihood Functions • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    + +
    + + + + +
    +
    +
    +

    +Examples

    +
    +
    +

    +Plot Publication-Ready Interval Functions

    +
    +

    +(Consonance, Surprisal, Likelihood, & Deviance)

    +

    +
    +
    +
    +
    +

    +Compare Interval Functions From Different Data/Studies

    +

    +
    +
    +
    +

    +Export Tabled Results Easily For Word, Powerpoint, & LaTeX

    +

    +
    +
    +
    +

    +Installation

    +
    +

    +For R:

    +
    +

    +Install the Package From CRAN

    +
    install.packages("concurve")
    +
    +
    +

    +Install the Developer Version

    +
    library(devtools)
    +install_github("zadchow/concurve")
    +
    +
    +

    +Check out the Examples.

    +
    +
    +
    +
    +

    +For Stata:

    +
    +

    +Check out the Article on Using Stata for concurve.

    +
    +
    +
    +
    +
    +

    +Dependencies

    +
      +
    • ggplot2
    • +
    • metafor
    • +
    • parallel
    • +
    • MASS
    • +
    • boot
    • +
    • bcaboot
    • +
    • compiler
    • +
    • ProfileLikelihood
    • +
    • pbmcapply
    • +
    • rlang
    • +
    • magrittr
    • +
    • dplyr
    • +
    • tidyr
    • +
    • knitr
    • +
    • flextable
    • +
    • officer
    • +
    • tibble
    • +
    • survival
    • +
    • survminer
    • +
    • scales
    • +
    +
    +
    +
    +

    +Purpose

    +
    +

    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

    +

    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

    +

    Following the common (and important) warning that P-values are not hypothesis probabilities, we caution that the P-value graph is not a probability distribution: It shows compatibility of parameter values with the data, rather than plausibility or probability of those values given the data. This is not a subtle difference: compatibility is a much weaker condition than plausibility. Consider for example that complete fabrication of the data is always an explanation compatible with the data (and indeed has happened in some influential medical studies [65]), but in studies with many participants and authors involved in all aspects of data collection it becomes so implausible or improbable as to not even merit mention. We emphasize then that all the P-value ever addresses in a direct logical sense is compatibility; for hypothesis probabilities one must turn to Bayesian methods [25].

    + +
    +
    +
    +

    In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

    +

    From a scientific perspective, the likelihood function and p-value function provide the basis for scientific judgments by an investigator, and by other investigators who might have interest. It thus replaces a blunt yes or no decision by an opportunity for appropriate informed judgment.” - Fraser, 2019

    +
    +
    +
    +

    Statistical software does not help you know what to compute, nor how to interpret the result. It does not offer to explain the assumptions behind methods, nor does it flag delicate or dubious assumptions. It does not warn you about multiplicity or p-hacking. It does not check whether you picked the hypothesis or analysis after looking at the data, nor track the number of analyses you tried before arriving at the one you sought to publish – another form of multiplicity. The more “powerful” and “user-friendly” the software is, the more it invites cargo-cult statistics." - Stark & Saltelli, 2018

    +
    +
    +
    +

    +References

    +
      +
    1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:1909.08579 [stat.ME]. 2019
    2. +
    3. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics. arXiv:1909.08583 [stat.ME]. 2019
    4. +
    5. Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.
    6. +
    7. Sullivan KM, Foster DA. Use of the confidence interval function._Epidemiology._ 1990;1(1):39-42.
    8. +
    9. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012.
    10. +
    11. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv [mathST]. 2007.
    12. +
    13. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332.
    14. +
    15. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There is No Replication Crisis if We Don’t Expect Replication. Am Stat. 2019
    16. +
    17. Greenland S. Valid P-values Behave Exactly As They Should. Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2019;18(136).
    18. +
    19. Fraser DAS. The p-value Function and Statistical Inference. Am Stat. 2019
    20. +
    21. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15(4):40-43.
    22. +
    +
    + + +
    + + +
    + + + +
    + + + + + diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 0000000..88ad827 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/logo.svg b/docs/logo.svg new file mode 100644 index 0000000..f42ed9b --- /dev/null +++ b/docs/logo.svg @@ -0,0 +1,93 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/news/index.html b/docs/news/index.html new file mode 100644 index 0000000..9f0dbf3 --- /dev/null +++ b/docs/news/index.html @@ -0,0 +1,330 @@ + + + + + + + + +Changelog • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +concurve 3.0 Unreleased +

    +
    +

    +Major changes

    +
      +
    • +ggconcurve() plots confidence (consonance) distributions, densities, and likelihood functions.
      +
    • +
    • +curve_compare() compares two functions and calculates the area between the curve.
    • +
    • +plot_compare()allows two separate functions to be plotted and compared simultaneously.
    • +
    • +curve_table() produces publication-ready tables of relevant statistics.
    • +
    • +curve_boot() uses bootstrapping to approximate the consonance functions.
    • +
    • +curve_lik() produces likelihood functions by transforming the objects from the ProfileLikelihood package.
    • +
    +
    +
    +

    +Minor changes

    +
      +
    • All functions now provide progress on how long it will take to complete the task.
    • +
    • Interval widths are now provided as measures of precision.
    • +
    +
    +
    +
    +

    +concurve 2.1.0 2019-09-19 +

    +
    +

    +Major changes

    +
      +
    • +ggconcurve() now plots both the P-values and CI level using both y-axes when the type = “consonance”. Previously, this was only possible via plot_concurve() (which uses base R graphics) because ggplot2 had a bug in its last few versions, which inhibited proper transformations in the y-axis.
    • +
    +
    +
    +
    +

    +concurve 2.0.1 Unreleased +

    +
    +

    +Major changes

    +
      +
    • +plot_concurve() now has “measure” as an item which allows for ratio measures to be logarithmically scaled on the x axis. There are two options, “default”, which is set as the default option and is for mean differences, and “ratio”, which will result in the axis being logarithmically scaled.
    • +
    • +plot_concurve() also now has a “fill” option which will allow users to choose the color of the plot.
    • +
    +
    +
    +
    +

    +concurve 2.0 2019-07-10 +

    +
    +

    +Major changes

    +
      +
    • The plotpint() function which plotted consonance functions has been repackaged into ggconcurve().
    • +
    • The plotsint() function which plotted surprisal functions has been repackaged into ggconcurve().
    • +
    • Functions can now also be plotted with base R via the plot_concurve() function.
    • +
    • Consonance functions can be plotted as a pyramid (right side up) or inverted (upside down) via the “position” item in ggconcurve().
    • +
    • Null values (for means & ratios) can be plotted via the ggconcurve() function to show how much of the interval surrounds it.
    • +
    • Log transformations included in all the plotting functions for ratio measures.
    • +
    • Parallel programming has now been implemented into the computations via the mclapply() function from the parallel package.
    • +
    +
    +
    +
    +

    +concurve 1.08 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Can produce consonance and surprisal functions for correlations via the corrintervals() function.
    • +
    • Now able to construct consonance and surprisal functions from the point estiate, and confidence limits via the rev_eng() function.
    • +
    • Graphs produced via the plotpint() or plotsint() function now able to take custom titles, subtitles, x-axis titles, and captions.
    • +
    +
    +
    +
    +

    +concurve 1.07 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Can now produce consonance and surprisal functions for survival data produced with the survival package.
    • +
    +
    +
    +
    +

    +concurve 1.06 Unreleased +

    +
    +

    +Major changes

    +
      +
    • Now contains documentation for producing interval functons in Stata.
    • +
    +
    +
    +

    +Minor changes

    +
      +
    • Default plots now contain grids, title, subtitle, and a caption.
    • +
    • Updated figures in README and the ‘Examples in R’ vignette/article.
    • +
    +
    +
    +
    + + + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/pkgdown.css b/docs/pkgdown.css new file mode 100644 index 0000000..9145958 --- /dev/null +++ b/docs/pkgdown.css @@ -0,0 +1,256 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; + color: #666; + display: flex; + flex-shrink: 0; +} +footer p { + margin-bottom: 0; +} +footer div { + flex: 1; +} +footer .pkgdown { + text-align: right; +} +footer p { + margin-bottom: 0; +} + +img.icon { + float: right; +} + +img { + max-width: 100%; +} + +/* Fix bug in bootstrap (only seen in firefox) */ +summary { + display: list-item; +} + +/* Typographic tweaking ---------------------------------*/ + +.contents .page-header { + margin-top: calc(-60px + 1em); +} + +/* Section anchors ---------------------------------*/ + +a.anchor { + margin-left: -30px; + display:inline-block; + width: 30px; + height: 30px; + visibility: hidden; + + background-image: url(./link.svg); + background-repeat: no-repeat; + background-size: 20px 20px; + background-position: center center; +} + +.hasAnchor:hover a.anchor { + visibility: visible; +} + +@media (max-width: 767px) { + .hasAnchor:hover a.anchor { + visibility: hidden; + } +} + + +/* Fixes for fixed navbar --------------------------*/ + +.contents h1, .contents h2, .contents h3, .contents h4 { + padding-top: 60px; + margin-top: -40px; +} + +/* Sidebar --------------------------*/ + +#sidebar { + margin-top: 30px; + position: -webkit-sticky; + position: sticky; + top: 70px; +} +#sidebar h2 { + font-size: 1.5em; + margin-top: 1em; +} + +#sidebar h2:first-child { + margin-top: 0; +} + +#sidebar .list-unstyled li { + margin-bottom: 0.5em; +} + +.orcid { + height: 16px; + /* margins are required by official ORCID trademark and display guidelines */ + margin-left:4px; + margin-right:4px; + vertical-align: middle; +} + +/* Reference index & topics ----------------------------------------------- */ + +.ref-index th {font-weight: normal;} + +.ref-index td {vertical-align: top;} +.ref-index .icon {width: 40px;} +.ref-index .alias {width: 40%;} +.ref-index-icons .alias {width: calc(40% - 40px);} +.ref-index .title {width: 60%;} + +.ref-arguments th {text-align: right; padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + +/* Syntax highlighting ---------------------------------------------------- */ + +pre { + word-wrap: normal; + word-break: normal; + border: 1px solid #eee; +} + +pre, code { + background-color: #f8f8f8; + color: #333; +} + +pre code { + overflow: auto; + word-wrap: normal; + white-space: pre; +} + +pre .img { + margin: 5px 0; +} + +pre .img img { + background-color: #fff; + display: block; + height: auto; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.message { color: black; font-weight: bolder;} +.error { color: orange; font-weight: bolder;} +.warning { color: #6A0366; font-weight: bolder;} + +/* Clipboard --------------------------*/ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; +} + +/* headroom.js ------------------------ */ + +.headroom { + will-change: transform; + transition: transform 200ms linear; +} +.headroom--pinned { + transform: translateY(0%); +} +.headroom--unpinned { + transform: translateY(-100%); +} + +/* mark.js ----------------------------*/ + +mark { + background-color: rgba(255, 255, 51, 0.5); + border-bottom: 2px solid rgba(255, 153, 51, 0.3); + padding: 1px; +} + +/* vertical spacing after htmlwidgets */ +.html-widget { + margin-bottom: 10px; +} + +/* fontawesome ------------------------ */ + +.fab { + font-family: "Font Awesome 5 Brands" !important; +} + +/* don't display links in code chunks when printing */ +/* source: https://stackoverflow.com/a/10781533 */ +@media print { + code a:link:after, code a:visited:after { + content: ""; + } +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js new file mode 100644 index 0000000..087a762 --- /dev/null +++ b/docs/pkgdown.js @@ -0,0 +1,113 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('.navbar-fixed-top').headroom(); + + $('body').css('padding-top', $('.navbar').height() + 10); + $(window).resize(function(){ + $('body').css('padding-top', $('.navbar').height() + 10); + }); + + $('body').scrollspy({ + target: '#sidebar', + offset: 60 + }); + + $('[data-toggle="tooltip"]').tooltip(); + + var cur_path = paths(location.pathname); + var links = $("#navbar ul li a"); + var max_length = -1; + var pos = -1; + for (var i = 0; i < links.length; i++) { + if (links[i].getAttribute("href") === "#") + continue; + // Ignore external links + if (links[i].host !== location.host) + continue; + + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
    +
    + + + + +
    + +
    +
    + + +
    +

    All vignettes

    +

    + + +
    +
    +
    + + + +
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $(".examples, div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 0000000..c4bf0d4 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,10 @@ +pandoc: '2.6' +pkgdown: 1.4.1 +pkgdown_sha: ~ +articles: + examples: examples.html + stata: stata.html +urls: + reference: https://data.lesslikely.com/concurve//reference + article: https://data.lesslikely.com/concurve//articles + diff --git a/docs/reference/concurve-package.html b/docs/reference/concurve-package.html new file mode 100644 index 0000000..b0cf55e --- /dev/null +++ b/docs/reference/concurve-package.html @@ -0,0 +1,259 @@ + + + + + + + + +concurve: Computes and Plots Compatibility (Confidence) Intervals, + P-Values, S-Values, & Likelihood Intervals to Form Consonance, + Surprisal, & Likelihood Functions — concurve-package • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    logo

    +

    Allows one to compute consonance (confidence) + intervals for various statistical tests along with their corresponding + P-values, S-values, and likelihoods. The intervals can be plotted to + create consonance, surprisal, and likelihood functions allowing one to + see what effect sizes are compatible with the test model at various + consonance levels rather than being limited to one interval estimate + such as 95 + <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) + <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) + <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2008, + ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) + <doi:10.1080/00031305.2018.1543137>, Greenland S. (2019) + <doi:10.1080/00031305.2018.1529625>, Chow ZR, Greenland S. (2019) + <arXiv:1909.08579>, and Greenland S, Chow ZR. (2019) + <arXiv:1909.08583>.

    +
    + + + +

    See also

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zU2T35TJydU;lxaB@y5-+w{MH3?c{wu^1;dc5+BohS(UWBkh)5CPS?9r$_18ac;6dQ za9F?KO)R)Pek*hbX)b%)qXcbe&S|GioHK_z{W)cS{Nr5yil#mk@s=S4&vN)E|0Yyr zsGE~mHB=gZA0trw@MCuXNU?pzp=Q~*% z@sK{SokRZubl$B;V*12L(k}X7ld?<#T4Rxr=Rf5Lhs;OkqdE_Zz`#0HCC%NXJ&}AugpHa(={HyMOVw&JoETt z<2#s$)PGO_`_*2f7Uq3BE7`Hz1Ny|(N-p|r0bHvLwg>^MqN6SRwP;t)iVX(xWi>cn zI{HdE>x6^xcDglW%?-no`{YgNACs65xD>^r*dpQ2o%4E;3A+{tNnpJccvKy$X zsq>zl8Klx77Fa@tOplcH45#M63rI8op;Vz(TC`!Ne zehiLS7@O`iHcgG}&K>d!XH~CMT%Z&pF{?X!om*1#h)%PTB0q#mG_u^Tayp$^Rt?8m z@JN-fnp*A>=aj0DxAXV0!OI+rWMM6TMmxx!eSf?0adc6rp~;k^l7dSGdy4&T0l*^9iLZNA&i06 zhOpNC4VD=xYd8F!e+`!MFxFKH0(~O=@Z928NTG_tbVvlYE_C1gTmBVA7|9}{+<}pb zkPHAa1rmY#Pz2xEtb&bZqe=svEn)`j#2$KAABA&Kt;-BxsJ2-5ufpkB;L`NQ?W4f~ eOD=;aIR42uiK{iWd1zNL0D-5gpUXO@geCxmjTwyq literal 0 HcmV?d00001 diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html new file mode 100644 index 0000000..5883c96 --- /dev/null +++ b/docs/reference/curve_compare.html @@ -0,0 +1,268 @@ + + + + + + + + +Compares two functions and produces an AUC score to show the amount of consonance. — curve_compare • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +
    + + + + +
    + +
    +
    + + +
    +

    Compares the p-value/s-value, and likelihood functions and computes an AUC number.

    +
    + +
    curve_compare(data1, data2, type = "c", plot = TRUE, ...)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    data1

    The first dataframe produced by one of the interval functions +in which the intervals are stored.

    data2

    The second dataframe produced by one of the interval functions in +which the intervals are stored.

    type

    Choose whether to plot a "consonance" function, a "surprisal" function or +"likelihood". The default option is set to "c". The type must be set in quotes, +for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options +include "pd" for the consonance distribution function, and "cd" for the consonance +density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" +for likelihood and "d" for deviance function.

    plot

    by default it is set to TRUE and will use the plot_compare() function +to plot the two functions.

    ...

    Can be used to pass further arguments to plot_compare().

    + + +

    Examples

    +
    # \donttest{ +library(concurve) +GroupA <- rnorm(500) +GroupB <- rnorm(500) +RandomData <- data.frame(GroupA, GroupB) +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) +RandomData2 <- data.frame(GroupA2, GroupB2) +model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2", method = "glm") +(curve_compare(intervalsdf[[1]], randomframe[[1]]))
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] +#> +#> +#> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| +#> |-----:|-----:|----------:|---------------:|-----------------------------:| +#> | 0.102| 0.072| 0.023| 0.151| 0.178| +#> +#> [[2]]
    #>
    (curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] +#> +#> +#> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| +#> |-----:|-----:|----------:|---------------:|-----------------------------:| +#> | 4.115| 1.522| 1.522| 0.37| 0.587| +#> +#> [[2]]
    #>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html new file mode 100644 index 0000000..a6665c2 --- /dev/null +++ b/docs/reference/curve_corr.html @@ -0,0 +1,275 @@ + + + + + + + + +Computes Consonance Intervals for Correlations — curve_corr • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes consonance intervals to produce P- and S-value functions for +correlational analysesusing the cor.test function in base R and places the +interval limits for each interval levelinto a data frame along with the +corresponding p-values and s-values.

    +
    + +
    curve_corr(x, y, alternative, method, steps = 10000, table = TRUE)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    x

    A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.

    y

    A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.

    alternative

    Indicates the alternative hypothesis and must be one of "two.sided", +"greater" or "less". You can specify just the initial letter. "greater" corresponds to +positive association, "less" to negative association.

    method

    A character string indicating which correlation coefficient is +to be used for the test. One of "pearson", "kendall", or "spearman", +can be abbreviated.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    # \donttest{ +GroupA <- rnorm(50) +GroupB <- rnorm(50) +joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") +tibble::tibble(joe[[1]])
    #> # A tibble: 10,000 x 1 +#> `joe[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.143 0.143 0 0 0.5 1 +#> 2 0.143 0.143 0.0000358 0.0001 0.500 1.000 +#> 3 0.143 0.143 0.0000716 0.0002 0.500 1.000 +#> 4 0.143 0.143 0.000107 0.000300 0.500 1.000 +#> 5 0.143 0.143 0.000143 0.0004 0.500 1.000 +#> 6 0.143 0.143 0.000179 0.0005 0.500 1.000 +#> 7 0.143 0.143 0.000215 0.000600 0.500 0.999 +#> 8 0.143 0.143 0.000251 0.0007 0.500 0.999 +#> 9 0.143 0.143 0.000287 0.0008 0.500 0.999 +#> 10 0.143 0.143 0.000322 0.0009 0.500 0.999 +#> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html new file mode 100644 index 0000000..501baa5 --- /dev/null +++ b/docs/reference/curve_gen.html @@ -0,0 +1,283 @@ + + + + + + + + +General Consonance Functions Using Profile Likelihood, Wald, +or the bootstrap method for linear models. — curve_gen • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for +the chosen parameter in the selected model +(ANOVA, ANCOVA, regression, logistic regression) and places +the interval limits for each interval level into a data frame along +with the corresponding p-values and s-values.

    +
    + +
    curve_gen(model, var, method = "wald", steps = 1000, table = TRUE)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + +
    model

    The statistical model of interest +(ANOVA, regression, logistic regression) is to be indicated here.

    var

    The variable of interest from the model (coefficients, intercept) +for which the intervals are to be produced.

    method

    Chooses the method to be used to calculate the +consonance intervals. There are currently four methods: +"default", "wald", "lm", and "boot". The "default" method uses the profile +likelihood method to compute intervals and can be used for models created by +the 'lm' function. The "wald" method is typicallywhat most people are +familiar with when computing intervals based on the calculated standard error. +The "lm" method allows this function to be used for specific scenarios like +logistic regression and the 'glm' function. The "boot" method allows for +bootstrapping at certain levels.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    # \donttest{ +# Simulate random data +GroupA <- rnorm(50) +GroupB <- rnorm(50) +RandomData <- data.frame(GroupA, GroupB) +rob <- glm(GroupA ~ GroupB, data = RandomData) +bob <- curve_gen(rob, "GroupB", method = "glm") +tibble::tibble(bob[[1]])
    #> # A tibble: 998 x 1 +#> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.0876 -0.0872 0.000396 0.001 0.500 0.999 +#> 2 -0.0878 -0.0870 0.000793 0.002 0.501 0.998 +#> 3 -0.0880 -0.0868 0.00119 0.003 0.501 0.997 +#> 4 -0.0882 -0.0866 0.00159 0.004 0.502 0.996 +#> 5 -0.0884 -0.0864 0.00198 0.005 0.502 0.995 +#> 6 -0.0886 -0.0862 0.00238 0.006 0.503 0.994 +#> 7 -0.0887 -0.0860 0.00277 0.007 0.503 0.993 +#> 8 -0.0889 -0.0858 0.00317 0.008 0.504 0.992 +#> 9 -0.0891 -0.0856 0.00357 0.009 0.504 0.991 +#> 10 -0.0893 -0.0854 0.00396 0.01 0.505 0.99 +#> # … with 988 more rows, and 1 more variable: $svalue <dbl>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html new file mode 100644 index 0000000..36b10fe --- /dev/null +++ b/docs/reference/curve_lik.html @@ -0,0 +1,244 @@ + + + + + + + + +Compute the Profile Likelihood Functions — curve_lik • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Compute the Profile Likelihood Functions

    +
    + +
    curve_lik(likobject, data, table = TRUE)
    + +

    Arguments

    + + + + + + + + + + + + + + +
    likobject

    An object from the ProfileLikelihood package

    data

    The dataframe that was used to create the likelihood +object in the ProfileLikelihood package.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    library(ProfileLikelihood)
    #> Loading required package: nlme
    #> Loading required package: MASS
    data(dataglm) +xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit"))
    #> Warning message: provide lo.theta and hi.theta
    lik <- curve_lik(xx, dataglm) +tibble::tibble(lik[[1]])
    #> # A tibble: 300 x 1 +#> `lik[[1]]`$values $likelihood $loglikelihood $support $deviancestat +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -1.41 9.26e-21 -9.79 0.0000560 9.79 +#> 2 -1.39 1.05e-20 -9.66 0.0000638 9.66 +#> 3 -1.37 1.20e-20 -9.53 0.0000726 9.53 +#> 4 -1.35 1.37e-20 -9.40 0.0000826 9.40 +#> 5 -1.34 1.55e-20 -9.27 0.0000940 9.27 +#> 6 -1.32 1.76e-20 -9.15 0.000107 9.15 +#> 7 -1.30 2.00e-20 -9.02 0.000121 9.02 +#> 8 -1.28 2.27e-20 -8.89 0.000137 8.89 +#> 9 -1.26 2.57e-20 -8.77 0.000156 8.77 +#> 10 -1.24 2.92e-20 -8.64 0.000176 8.64 +#> # … with 290 more rows
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html new file mode 100644 index 0000000..6b44c5c --- /dev/null +++ b/docs/reference/curve_mean.html @@ -0,0 +1,294 @@ + + + + + + + + +Mean Interval Consonance Function — curve_mean • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen +parameter in a statistical test that compares means and places the interval +limits for each interval level into a data frame along with the corresponding +p-values and s-values.

    +
    + +
    curve_mean(
    +  x,
    +  y,
    +  data,
    +  paired = F,
    +  method = "default",
    +  replicates = 1000,
    +  steps = 10000,
    +  table = TRUE
    +)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    x

    Variable that contains the data for the first group being compared.

    y

    Variable that contains the data for the second group being compared.

    data

    Data frame from which the variables are being extracted from.

    paired

    Indicates whether the statistical test is a paired difference test. +By default, it is set to "F",which means the function will be an unpaired +statistical test comparing two independent groups.Inserting "paired" will +change the test to a paired difference test.

    method

    By default this is turned off (set to "default"), but +allows for bootstrapping if "boot" is insertedinto the function call.

    replicates

    Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    # \donttest{ +# Simulate random data +GroupA <- runif(100, min = 0, max = 100) +GroupB <- runif(100, min = 0, max = 100) +RandomData <- data.frame(GroupA, GroupB) +bob <- curve_mean(GroupA, GroupB, RandomData) +tibble::tibble(bob[[1]])
    #> # A tibble: 10,000 x 1 +#> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 4.18 4.18 0 0 0.5 1 +#> 2 4.18 4.18 0.00105 0.0001 0.500 1.000 +#> 3 4.18 4.18 0.00210 0.0002 0.500 1.000 +#> 4 4.18 4.18 0.00315 0.000300 0.500 1.000 +#> 5 4.18 4.18 0.00420 0.0004 0.500 1.000 +#> 6 4.18 4.18 0.00525 0.0005 0.500 1.000 +#> 7 4.18 4.18 0.00630 0.000600 0.500 0.999 +#> 8 4.18 4.18 0.00735 0.0007 0.500 0.999 +#> 9 4.18 4.18 0.00840 0.0008 0.500 0.999 +#> 10 4.18 4.19 0.00945 0.0009 0.500 0.999 +#> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html new file mode 100644 index 0000000..9d32e48 --- /dev/null +++ b/docs/reference/curve_meta.html @@ -0,0 +1,306 @@ + + + + + + + + +Meta-analytic Consonance Function — curve_meta • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen +parameter in the meta-analysis done by the metafor package and places the +interval limits for each interval level into a data frame along with the +corresponding p-values and s-values.

    +
    + +
    curve_meta(x, measure = "default", steps = 10000, table = TRUE)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    x

    Object where the meta-analysis parameters are stored, typically a +list produced by 'metafor'

    measure

    Indicates whether the object has a log transformation or is normal/default. +The default setting is "default. If the measure is set to "ratio", it will take +logarithmically transformed values and convert them back to normal values in the dataframe. +This is typically a setting used for binary outcomes such as risk ratios, +hazard ratios, and odds ratios.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    # \donttest{ +# Simulate random data for two groups in two studies +GroupAData <- runif(20, min = 0, max = 100) +GroupAMean <- round(mean(GroupAData), digits = 2) +GroupASD <- round(sd(GroupAData), digits = 2) + +GroupBData <- runif(20, min = 0, max = 100) +GroupBMean <- round(mean(GroupBData), digits = 2) +GroupBSD <- round(sd(GroupBData), digits = 2) + +GroupCData <- runif(20, min = 0, max = 100) +GroupCMean <- round(mean(GroupCData), digits = 2) +GroupCSD <- round(sd(GroupCData), digits = 2) + +GroupDData <- runif(20, min = 0, max = 100) +GroupDMean <- round(mean(GroupDData), digits = 2) +GroupDSD <- round(sd(GroupDData), digits = 2) + +# Combine the data + +StudyName <- c("Study1", "Study2") +MeanTreatment <- c(GroupAMean, GroupCMean) +MeanControl <- c(GroupBMean, GroupDMean) +SDTreatment <- c(GroupASD, GroupCSD) +SDControl <- c(GroupBSD, GroupDSD) +NTreatment <- c(20, 20) +NControl <- c(20, 20) + +metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl) + +# Use metafor to calculate the standardized mean difference + +library(metafor)
    #> Loading required package: Matrix
    #> Loading 'metafor' package (version 2.1-0). For an overview +#> and introduction to the package please type: help(metafor).
    # +dat <- escalc(measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf) +# +# Pool the data using a particular method. Here "FE" is the fixed-effects model +# +res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) +# +# Calculate the intervals using the metainterval function +# +metaf <- curve_meta(res) +# +tibble::tibble(metaf[[1]])
    #> # A tibble: 10,000 x 1 +#> `metaf[[1]]`$lo… $upper.limit $intrvl.level $pvalue $svalue $intrvl.width +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.159 0.159 0 1 0. 0 +#> 2 0.159 0.159 0.0001 1.000 1.44e-4 0.0000565 +#> 3 0.159 0.159 0.0002 1.000 2.89e-4 0.000113 +#> 4 0.159 0.159 0.000300 1.000 4.33e-4 0.000169 +#> 5 0.159 0.159 0.0004 1.000 5.77e-4 0.000226 +#> 6 0.159 0.159 0.0005 1.000 7.22e-4 0.000282 +#> 7 0.159 0.159 0.000600 0.999 8.66e-4 0.000339 +#> 8 0.159 0.159 0.0007 0.999 1.01e-3 0.000395 +#> 9 0.159 0.159 0.0008 0.999 1.15e-3 0.000452 +#> 10 0.159 0.159 0.0009 0.999 1.30e-3 0.000508 +#> # … with 9,990 more rows, and 1 more variable: $cdf <dbl>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html new file mode 100644 index 0000000..7187c2f --- /dev/null +++ b/docs/reference/curve_rev.html @@ -0,0 +1,286 @@ + + + + + + + + +Reverse Engineer Consonance / Likelihood Functions Using the Point +Estimate and Confidence Limits — curve_rev • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Using the confidence limits and point estimates from a dataset, one can use +these estimates to compute thousands of consonance intervals and graph the +intervals to form a consonance and surprisal function.

    +
    + +
    curve_rev(
    +  point,
    +  LL,
    +  UL,
    +  type = "c",
    +  measure = "default",
    +  steps = 10000,
    +  table = TRUE
    +)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    point

    The point estimate from an analysis. Ex: 1.20

    LL

    The lower confidence limit from an analysis Ex: 1.0

    UL

    The upper confidence limit from an analysis Ex: 1.4

    type

    Indicates whether the produced result should be a consonance +function or a likelihood function. The default is "c" for consonance and +likelihood can be set via "l".

    measure

    The type of data being used. If they involve mean differences,

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +

    Examples

    +
    # \donttest{ +# From a real published study. Point estimate of the result was hazard ratio of 1.61 and +# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. +# +df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") + +tibble::tibble(df[[1]])
    #> # A tibble: 9,999 x 1 +#> `df[[1]]`$lower… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.624 4.15 3.53 1.000 1.000 10.00e-5 +#> 2 0.651 3.98 3.33 1.000 1.000 2.00e-4 +#> 3 0.667 3.88 3.22 1.000 1.000 3.00e-4 +#> 4 0.680 3.81 3.13 1.000 1.000 4.00e-4 +#> 5 0.690 3.76 3.07 1.000 1.000 5.00e-4 +#> 6 0.698 3.71 3.02 0.999 1.000 6.00e-4 +#> 7 0.705 3.68 2.97 0.999 1.000 7.00e-4 +#> 8 0.712 3.64 2.93 0.999 1.000 8.00e-4 +#> 9 0.717 3.61 2.90 0.999 1.000 9.00e-4 +#> 10 0.722 3.59 2.87 0.999 1.000 1.00e-3 +#> # … with 9,989 more rows, and 1 more variable: $svalue <dbl>
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html new file mode 100644 index 0000000..773352a --- /dev/null +++ b/docs/reference/curve_surv.html @@ -0,0 +1,242 @@ + + + + + + + + +Produce Consonance Intervals for Survival Data — curve_surv • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Computes thousands of consonance (confidence) intervals for the chosen +parameter in the Cox model computed by the 'survival' package and places +the interval limits for each interval level into a data frame along +with the corresponding p-value and s-value.

    +
    + +
    curve_surv(data, x, steps = 10000, table = TRUE)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    data

    Object where the Cox model is stored, typically a list produced by the +'survival' package.

    x

    Predictor of interest within the survival model for which the +consonance intervals should be computed.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + + +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html new file mode 100644 index 0000000..c309094 --- /dev/null +++ b/docs/reference/curve_table.html @@ -0,0 +1,293 @@ + + + + + + + + +Produce Tables For concurve Functions — curve_table • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Produces publication-ready tables with relevant statistics of interest +for functions produced from the concurve package.

    +
    + +
    curve_table(data, levels, type = "c", format = "data.frame")
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + +
    data

    Dataframe from a concurve function to produce a table for

    levels

    Levels of the consonance intervals or likelihood intervals that should be +included in the table.

    type

    Indicates whether the table is for a consonance function or likelihood function. +The default is set to "c" for consonance and can be switched to "l" for likelihood.

    format

    The format of the tables. The options include "data.frame" which is the +default, "tibble", "docx" (which creates a table for a word document), "pptx" (which +creates a table for powerpoint), "latex", (which creates a table for a TeX document), and +"image", which produces an image of the table.

    + + +

    Examples

    +
    # \donttest{ + +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") + +(z <- curve_table(intervalsdf[[1]], format = "data.frame"))
    #> Lower Limit Upper Limit Interval Width Interval Level (%) CDF P-value +#> 2501 -0.039 0.002 0.041 25.0 0.625 0.750 +#> 5001 -0.062 0.025 0.087 50.0 0.750 0.500 +#> 7501 -0.093 0.055 0.148 75.0 0.875 0.250 +#> 8001 -0.101 0.064 0.165 80.0 0.900 0.200 +#> 8501 -0.111 0.074 0.185 85.0 0.925 0.150 +#> 9001 -0.125 0.087 0.212 90.0 0.950 0.100 +#> 9501 -0.145 0.107 0.252 95.0 0.975 0.050 +#> 9751 -0.163 0.126 0.289 97.5 0.988 0.025 +#> 9901 -0.185 0.147 0.332 99.0 0.995 0.010 +#> S-value (bits) +#> 2501 0.415 +#> 5001 1.000 +#> 7501 2.000 +#> 8001 2.322 +#> 8501 2.737 +#> 9001 3.322 +#> 9501 4.322 +#> 9751 5.322 +#> 9901 6.644
    (z <- curve_table(intervalsdf[[1]], format = "tibble"))
    #> # A tibble: 9 x 1 +#> subdf$`Lower Li… $`Upper Limit` $`Interval Widt… $`Interval Leve… $CDF +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.039 0.002 0.041 25 0.625 +#> 2 -0.062 0.025 0.087 50 0.75 +#> 3 -0.093 0.055 0.148 75 0.875 +#> 4 -0.101 0.064 0.165 80 0.9 +#> 5 -0.111 0.074 0.185 85 0.925 +#> 6 -0.125 0.087 0.212 90 0.95 +#> 7 -0.145 0.107 0.252 95 0.975 +#> 8 -0.163 0.126 0.289 97.5 0.988 +#> 9 -0.185 0.147 0.332 99 0.995 +#> # … with 2 more variables: $`P-value` <dbl>, $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf[[1]], format = "latex"))
    #> +#> +#> | | Lower Limit| Upper Limit| Interval Width| Interval Level (%)| CDF| P-value| S-value (bits)| +#> |:----|-----------:|-----------:|--------------:|------------------:|-----:|-------:|--------------:| +#> |2501 | -0.039| 0.002| 0.041| 25.0| 0.625| 0.750| 0.415| +#> |5001 | -0.062| 0.025| 0.087| 50.0| 0.750| 0.500| 1.000| +#> |7501 | -0.093| 0.055| 0.148| 75.0| 0.875| 0.250| 2.000| +#> |8001 | -0.101| 0.064| 0.165| 80.0| 0.900| 0.200| 2.322| +#> |8501 | -0.111| 0.074| 0.185| 85.0| 0.925| 0.150| 2.737| +#> |9001 | -0.125| 0.087| 0.212| 90.0| 0.950| 0.100| 3.322| +#> |9501 | -0.145| 0.107| 0.252| 95.0| 0.975| 0.050| 4.322| +#> |9751 | -0.163| 0.126| 0.289| 97.5| 0.988| 0.025| 5.322| +#> |9901 | -0.185| 0.147| 0.332| 99.0| 0.995| 0.010| 6.644|
    # } + +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html new file mode 100644 index 0000000..072f44a --- /dev/null +++ b/docs/reference/defunct.html @@ -0,0 +1,268 @@ + + + + + + + + +Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +concurve. — defunct" /> + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    The functions listed below are deprecated. Please use the listed alternatives.

    +
    + +
    plotpint(...)
    +
    +plotsint(...)
    +
    +meanintervals(...)
    +
    +metaintervals(...)
    +
    +genintervals(...)
    +
    +corrintervals(...)
    +
    +survintervals(...)
    +
    +rev_eng(...)
    + + +

    plotpint

    + + + +

    For plotpint, use ggconcurve.

    +

    plotsint

    + + + +

    For plotsint, use ggconcurve.

    +

    meanintervals

    + + + +

    For meanintervals, use curve_mean.

    +

    metaintervals

    + + + +

    For metaintervals, use curve_meta.

    +

    genintervals

    + + + +

    For genintervals, use curve_gen.

    +

    corrintervals

    + + + +

    For corrintervals, use curve_corr.

    +

    survintervals

    + + + +

    For survintervals, use curve_surv.

    +

    rev_eng

    + + + +

    For rev_eng, use curve_rev.

    + +
    + +
    + + + +
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zl5gf05)`w4u-xx=zrfBnh`xIgci=+(wesEb0hW73wuvtS`(>?ewDUTnmxLvBTAfF9 zNUq~8kXm{QS4A;DP~=f2WYPDiaJ=)crp^f*cWb!8W|euRS*iQxDz55q2g%LeH-428 z#-_yJrKscU4R7B`MC6wW&8&OuqfYiVaNYrrK=-#h&T#FJm!8;!$7gJ15?z}(Vju9x zi)&ugYh$?o#&{P)N>JGr+xo*9gUu_{_RH?O}*2ZR#_@ zccydIA`)I*spF+1KDRG4-^vZpOIK2Fw)W77xRSN~dgP4O#&G?J53NOl6i_A_9vque zd1!M3=3*feOQAe+GJT?K_cwl|b;7XXtd##ndadVu8hJmfZC?p5j%V5b)qIs9*hd3N=RmCe1yUEUrs5?Sa~{ z&eN#QpJsHi%>a+qrN=J)`A4{}j{#bxz5I`lE=rV$iD|MXztsGP=gdN^e(!+uAvY_t zvPw8`P+jqY$f^FW_j>23Mb^5-!P{G)UiG)L%v~78BwkIC*7&vWCm?ZxqU0{!E$T6) zpJFG{(s$1T%ZKzQ>2uv%NjdVV-j{)8HSeM`6kT;Z36OOC&8)qh?hp#>IkH-{YO|6&Qx zBL}AMjZ83u_^%(1%pqB0;c+~fWc~7A{}y>#;1`TEhZX#}nDUwtx?k)K + + + + + + + +Plots the P-Value (Consonance), S-value (Surprisal), +and Likelihood Function via ggplot2 — ggconcurve • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    +
    + + + + +
    + +
    +
    + + +
    +

    Takes the dataframe produced by the interval functions and +plots the p-values/s-values, consonance (confidence) levels, and +the interval estimates to produce a p-value/s-value function +using ggplot2 graphics.

    +
    + +
    ggconcurve(
    +  data,
    +  type = "c",
    +  measure = "default",
    +  levels = 0.95,
    +  nullvalue = FALSE,
    +  position = "pyramid",
    +  title = "Interval Function",
    +  subtitle = "The function displays intervals at every level.",
    +  xaxis = expression(Theta ~ "Range of Values"),
    +  yaxis = "P-value",
    +  color = "#000000",
    +  fill = "#239a98"
    +)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    data

    The dataframe produced by one of the interval functions +in which the intervals are stored.

    type

    Choose whether to plot a "consonance" function, a +"surprisal" function or "likelihood". The default option is set to "c". +The type must be set in quotes, for example ggconcurve (type = "s") or +ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, +"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +and "d" for deviance function.

    measure

    Indicates whether the object has a log transformation +or is normal/default. The default setting is "default". If the measure +is set to "ratio", it will take logarithmically transformed values and +convert them back to normal values in the dataframe. This is typically a +setting used for binary outcomes and their measures such as risk ratios, +hazard ratios, and odds ratios.

    levels

    Indicates which interval levels should be plotted on the function. +By default it is set to 0.95 to plot the 95 +but more levels can be plotted by using the c() function for example, +levels = c(0.50, 0.75, 0.95).

    nullvalue

    Indicates whether the null value for the measure +should be plotted. By default, it is set to FALSE, meaning it will not be +plotted as a vertical line. Changing this to TRUE, will plot a vertical +line at 0 when the measure is set to " default" and a vertical line at +1 when the measure is set to "ratio". For example, +ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). +This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function. +By default, it is set to "pyramid", meaning the p-value function will +stand right side up, like a pyramid. However, it can also be inverted +via the option "inverted". This will also change the sequence of the +y-axes to match the orientation.This can be set as such, +ggconcurve(type = "c", data = df, position = "inverted").

    title

    A custom title for the graph. By default, it is +set to "Consonance Function". In order to set a title, it must +be in quotes. For example, ggconcurve(type = "c", +data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set +to "The function contains consonance/confidence intervals at every level +and the P-values." In order to set a subtitle, it must be in quotes. +For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, +it is set to "Range of Values. +In order to set a x-axis title, it must be in quotes. For example, +ggconcurve(type = "c", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, +it is set to "Consonance Level". +In order to set a y-axis title, it must be in quotes. For example, +ggconcurve(type = "c", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points +and the ribbons in the graph. By default, it is set to color = "#555555". +The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, color = "#333333").

    fill

    Item that allows the user to choose the color of the ribbons in the graph. +By default, it is set to fill = "#239a98". The inputs must be in quotes. For example, +ggconcurve(type = "c", data = x, fill = "#333333").

    + +

    Value

    + +

    Plot with intervals at every consonance level graphed with their corresponding +p-values and compatibility levels.

    + +

    Examples

    +
    # \donttest{ +# Simulate random data + +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +(function1 <- ggconcurve(type = "c", intervalsdf[[1]]))
    # } +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 0000000..0085a13 --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,289 @@ + + + + + + + + +Function reference • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    curve_boot()

    +

    Generate Consonance Functions via Bootstrapping

    +

    curve_compare()

    +

    Compares two functions and produces an AUC score to show the amount of consonance.

    +

    curve_corr()

    +

    Computes Consonance Intervals for Correlations

    +

    curve_gen()

    +

    General Consonance Functions Using Profile Likelihood, Wald, +or the bootstrap method for linear models.

    +

    curve_lik()

    +

    Compute the Profile Likelihood Functions

    +

    curve_mean()

    +

    Mean Interval Consonance Function

    +

    curve_meta()

    +

    Meta-analytic Consonance Function

    +

    curve_rev()

    +

    Reverse Engineer Consonance / Likelihood Functions Using the Point +Estimate and Confidence Limits

    +

    curve_surv()

    +

    Produce Consonance Intervals for Survival Data

    +

    curve_table()

    +

    Produce Tables For concurve Functions

    +

    ggconcurve()

    +

    Plots the P-Value (Consonance), S-value (Surprisal), +and Likelihood Function via ggplot2

    +

    plot_compare()

    +

    Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2

    +
    + + +
    + + + +
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z*|2^aAb36v0m#Y(7xm=d;{t!67lN;CAxHneOtvOm49T2c7@-2BB8N|VeK~4F6i%!B z_h1EISgt$uKR#a^NzV1>ElmD?%`J-r<|w#*eb3oVw5kU40OWv-GT)bN2nTpE5^7t* zb(DRxRjJ);s5!{{y=5F?jv6jF5;psh1`Fo z>Yi{r04`tD*rC+I@2u+o_+d!0d=Yl0`HZyn-<$aNrNW<@{&U6rAODB{|KtC^{P5k5 aPrrN1p4z5lb?pTFBPR4nFz11$`~L&u5+*VL literal 0 HcmV?d00001 diff --git a/docs/reference/plot_compare.html b/docs/reference/plot_compare.html new file mode 100644 index 0000000..73363c4 --- /dev/null +++ b/docs/reference/plot_compare.html @@ -0,0 +1,343 @@ + + + + + + + + +Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 — plot_compare • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Compares the p-value/s-value, and likelihood functions using ggplot2 graphics.

    +
    + +
    plot_compare(
    +  data1,
    +  data2,
    +  type = "c",
    +  measure = "default",
    +  nullvalue = FALSE,
    +  position = "pyramid",
    +  title = "Interval Functions",
    +  subtitle = "The function displays intervals at every level.",
    +  xaxis = expression(Theta ~ "Range of Values"),
    +  yaxis = "P-value",
    +  color = "#000000",
    +  fill1 = "#239a98",
    +  fill2 = "#d46c5b"
    +)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    data1

    The first dataframe produced by one of the interval functions in which the +intervals are stored.

    data2

    The second dataframe produced by one of the interval functions in which the +intervals are stored.

    type

    Choose whether to plot a "consonance" function, a +"surprisal" function or "likelihood". The default option is set to "c". +The type must be set in quotes, for example ggconcurve (type = "s") or +ggconcurve(type = "c"). Other options include "pd" for the consonance +distribution function, and "cd" for the consonance density function, +"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood +and "d" for deviance function.

    measure

    Indicates whether the object has a log transformation +or is normal/default. The default setting is "default". If the measure +is set to "ratio", it will take logarithmically transformed values and +convert them back to normal values in the dataframe. This is typically a +setting used for binary outcomes and their measures such as risk ratios, +hazard ratios, and odds ratios.

    nullvalue

    Indicates whether the null value for the measure +should be plotted. By default, it is set to FALSE, meaning it will not be +plotted as a vertical line. Changing this to TRUE, will plot a vertical +line at 0 when the measure is set to " default" and a vertical line at +1 when the measure is set to "ratio". For example, +ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). +This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function. +By default, it is set to "pyramid", meaning the p-value function will +stand right side up, like a pyramid. However, it can also be inverted +via the option "inverted". This will also change the sequence of the +y-axes to match the orientation.This can be set as such, +ggconcurve(type = "c", data = df, position = "inverted").

    title

    A custom title for the graph. By default, it is +set to "Consonance Function". In order to set a title, it must +be in quotes. For example, ggconcurve(type = "c", +data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set +to "The function contains consonance/confidence intervals at every level +and the P-values." In order to set a subtitle, it must be in quotes. +For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, +it is set to "Range of Values. +In order to set a x-axis title, it must be in quotes. For example, +ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, +it is set to "Consonance Level". +In order to set a y-axis title, it must be in quotes. For example, +ggconcurve(type = "c", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points +and the ribbons in the graph. By default, it is set to color = "#555555". +The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, color = "#333333").

    fill1

    Item that allows the user to choose the color of the ribbons in the graph +for data1. By default, it is set to fill1 = "#239a98". The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, fill1 = "#333333").

    fill2

    Item that allows the user to choose the color of the ribbons in the graph +for data1. By default, it is set to fill2 = "#d46c5b". The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, fill2 = "#333333").

    + +

    Value

    + +

    A plot that compares two functions.

    + +

    Examples

    +
    # \donttest{ +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) +RandomData <- data.frame(GroupA, GroupB) +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) +RandomData2 <- data.frame(GroupA2, GroupB2) +model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) + +(plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))
    # } + +
    +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/reference/tidyeval.html b/docs/reference/tidyeval.html new file mode 100644 index 0000000..80170e1 --- /dev/null +++ b/docs/reference/tidyeval.html @@ -0,0 +1,269 @@ + + + + + + + + +Tidy eval helpers — tidyeval • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    + +
      +
    • sym() creates a symbol from a string and +syms() creates a list of symbols from a +character vector.

    • +
    • enquo() and +enquos() delay the execution of one or +several function arguments. enquo() returns a single quoted +expression, which is like a blueprint for the delayed computation. +enquos() returns a list of such quoted expressions.

    • +
    • expr() quotes a new expression locally. It +is mostly useful to build new expressions around arguments +captured with enquo() or enquos(): +expr(mean(!!enquo(arg), na.rm = TRUE)).

    • +
    • as_name() transforms a quoted variable name +into a string. Supplying something else than a quoted variable +name is an error.

      +

      That's unlike as_label() which also returns +a single string but supports any kind of R object as input, +including quoted function calls and vectors. Its purpose is to +summarise that object into a single label. That label is often +suitable as a default name.

      +

      If you don't know what a quoted expression contains (for instance +expressions captured with enquo() could be a variable +name, a call to a function, or an unquoted constant), then use +as_label(). If you know you have quoted a simple variable +name, or would like to enforce this, use as_name().

    • +
    + +

    To learn more about tidy eval and how to use these tools, visit +https://tidyeval.tidyverse.org and the +Metaprogramming +section of Advanced R.

    +
    + + + + +
    + +
    + + + +
    + + + + + + + + + + + diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..56be766 --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,57 @@ + + + + https://data.lesslikely.com/concurve//index.html + + + https://data.lesslikely.com/concurve//reference/concurve-package.html + + + https://data.lesslikely.com/concurve//reference/curve_boot.html + + + https://data.lesslikely.com/concurve//reference/curve_compare.html + + + https://data.lesslikely.com/concurve//reference/curve_corr.html + + + https://data.lesslikely.com/concurve//reference/curve_gen.html + + + https://data.lesslikely.com/concurve//reference/curve_lik.html + + + https://data.lesslikely.com/concurve//reference/curve_mean.html + + + https://data.lesslikely.com/concurve//reference/curve_meta.html + + + https://data.lesslikely.com/concurve//reference/curve_rev.html + + + https://data.lesslikely.com/concurve//reference/curve_surv.html + + + https://data.lesslikely.com/concurve//reference/curve_table.html + + + https://data.lesslikely.com/concurve//reference/defunct.html + + + https://data.lesslikely.com/concurve//reference/ggconcurve.html + + + https://data.lesslikely.com/concurve//reference/plot_compare.html + + + https://data.lesslikely.com/concurve//reference/tidyeval.html + + + https://data.lesslikely.com/concurve//articles/examples.html + + + https://data.lesslikely.com/concurve//articles/stata.html + + diff --git a/examples/examples.R b/examples/examples.R index 035007a..cdde789 100644 --- a/examples/examples.R +++ b/examples/examples.R @@ -24,15 +24,29 @@ tibble::tibble(intervalsdf[[1]]) (function1s <- ggconcurve(data = intervalsdf[[2]], type = "cdf")) +options(mc.cores = 8L) +getOption("mc.cores", 2L) + GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) +model <- glm(GroupA2 ~ GroupB2, data = RandomData2) + +system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)) + +options(mc.cores = 2L) + +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) model <- glm(GroupA2 ~ GroupB2, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2", method = "glm") +system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)) + (function2 <- ggconcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) @@ -103,6 +117,9 @@ plot_compare(data1 = lik1[[1]], data2 = lik2[[1]], type = "d", measure = "ratio" # Bootstrapping ----------------------------------------------------------- +options(mc.cores = 8L) +getOption("mc.cores", 2L) + data(diabetes, package = "bcaboot") Xy <- cbind(diabetes$x, diabetes$y) rfun <- function(Xy) { @@ -111,7 +128,22 @@ rfun <- function(Xy) { return(summary(lm(y ~ X))$adj.r.squared) } -(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000)) +system.time(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 20000, steps = 1000)) + + +options(mc.cores = 2L) +getOption("mc.cores", 2L) + +data(diabetes, package = "bcaboot") +Xy <- cbind(diabetes$x, diabetes$y) +rfun <- function(Xy) { + y <- Xy[, 11] + X <- Xy[, 1:10] + return(summary(lm(y ~ X))$adj.r.squared) +} + +system.time(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 20000, steps = 1000)) + ggconcurve(data = x[[1]]) ggconcurve(data = x[[3]]) diff --git a/R/plot_concurve.R b/examples/plot_concurve.R similarity index 100% rename from R/plot_concurve.R rename to examples/plot_concurve.R diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd index a7f6520..5b0c73e 100644 --- a/man/curve_boot.Rd +++ b/man/curve_boot.Rd @@ -8,7 +8,7 @@ curve_boot( data = data, func = func, method = "bca", - replicates = 20000, + replicates = 2000, steps = 1000, table = TRUE ) @@ -46,17 +46,22 @@ Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot to generate consonance distrbutions. } \examples{ -library(Lock5Data) -dataz <- data(CommuteAtlanta) -func = function(data, index) { - x <- as.numeric(unlist(data[1])) - y <- as.numeric(unlist(data[2])) - return(mean(x[index]) - mean(y[index])) +\donttest{ +data(diabetes, package = "bcaboot") +Xy <- cbind(diabetes$x, diabetes$y) +rfun <- function(Xy) { + y <- Xy[, 11] + X <- Xy[, 1:10] + return(summary(lm(y ~ X))$adj.r.squared) } -z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) -ggconcurve(data = z[[1]]) -ggconcurve(data = z[[2]], type = "cd") +x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000) + +ggconcurve(data = x[[1]]) +ggconcurve(data = x[[3]]) + +plot_compare(x[[1]], x[[3]]) +} } diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd index 3b13398..bed8d4d 100644 --- a/man/curve_compare.Rd +++ b/man/curve_compare.Rd @@ -29,17 +29,18 @@ to plot the two functions.} Compares the p-value/s-value, and likelihood functions and computes an AUC number. } \examples{ +\donttest{ library(concurve) GroupA <- rnorm(500) GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) -(curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", plot = TRUE, measure = "default", nullvalue = TRUE)) -(curve_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", plot = TRUE, measure = "default", nullvalue = FALSE)) - +randomframe <- curve_gen(model, "GroupB2", method = "glm") +(curve_compare(intervalsdf[[1]], randomframe[[1]])) +(curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) +} } diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd index 5212048..c3f9e47 100644 --- a/man/curve_corr.Rd +++ b/man/curve_corr.Rd @@ -31,9 +31,6 @@ store them into a dataframe.} \item{table}{Indicates whether or not a table output with some relevant statistics should be generated. The default is TRUE and generates a table which is included in the list object.} -} -\value{ - } \description{ Computes consonance intervals to produce P- and S-value functions for @@ -42,6 +39,7 @@ interval limits for each interval levelinto a data frame along with the corresponding p-values and s-values. } \examples{ + GroupA <- rnorm(50) GroupB <- rnorm(50) joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd index 40b155f..4e3b6a5 100644 --- a/man/curve_gen.Rd +++ b/man/curve_gen.Rd @@ -34,9 +34,6 @@ store them into a dataframe.} \item{table}{Indicates whether or not a table output with some relevant statistics should be generated. The default is TRUE and generates a table which is included in the list object.} -} -\value{ - } \description{ Computes thousands of consonance (confidence) intervals for @@ -46,6 +43,8 @@ the interval limits for each interval level into a data frame along with the corresponding p-values and s-values. } \examples{ + +\donttest{ # Simulate random data GroupA <- rnorm(50) GroupB <- rnorm(50) @@ -54,3 +53,5 @@ rob <- glm(GroupA ~ GroupB, data = RandomData) bob <- curve_gen(rob, "GroupB", method = "glm") tibble::tibble(bob[[1]]) } + +} diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd index 9816ef7..aa9f973 100644 --- a/man/curve_lik.Rd +++ b/man/curve_lik.Rd @@ -20,9 +20,11 @@ which is included in the list object.} Compute the Profile Likelihood Functions } \examples{ + library(ProfileLikelihood) data(dataglm) -xx <- profilelike.glm(y ~ x1 + x2, data = dataglm, profile.theta = "group", family = binomial(link = "logit"), length = 500, round = 2) +xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit")) lik <- curve_lik(xx, dataglm) tibble::tibble(lik[[1]]) + } diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd index a51a5da..96bd0d8 100644 --- a/man/curve_mean.Rd +++ b/man/curve_mean.Rd @@ -59,4 +59,5 @@ GroupB <- runif(100, min = 0, max = 100) RandomData <- data.frame(GroupA, GroupB) bob <- curve_mean(GroupA, GroupB, RandomData) tibble::tibble(bob[[1]]) + } diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd index dad11bf..096f8ad 100644 --- a/man/curve_meta.Rd +++ b/man/curve_meta.Rd @@ -35,7 +35,7 @@ corresponding p-values and s-values. } \examples{ -# Simulate random data for two groups in two studies +# Simulate random data for two groups in two studies GroupAData <- runif(20, min = 0, max = 100) GroupAMean <- round(mean(GroupAData), digits = 2) GroupASD <- round(sd(GroupAData), digits = 2) @@ -79,4 +79,5 @@ res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE metaf <- curve_meta(res) # tibble::tibble(metaf[[1]]) + } diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd index 6ba3cc0..fbfbbf6 100644 --- a/man/curve_rev.Rd +++ b/man/curve_rev.Rd @@ -52,4 +52,5 @@ intervals to form a consonance and surprisal function. df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") tibble::tibble(df[[1]]) + } diff --git a/man/curve_surv.Rd b/man/curve_surv.Rd index 76ae67e..2f91d5b 100644 --- a/man/curve_surv.Rd +++ b/man/curve_surv.Rd @@ -30,6 +30,3 @@ parameter in the Cox model computed by the 'survival' package and places the interval limits for each interval level into a data frame along with the corresponding p-value and s-value. } -\examples{ - -} diff --git a/man/curve_table.Rd b/man/curve_table.Rd index b1ff840..1e565d0 100644 --- a/man/curve_table.Rd +++ b/man/curve_table.Rd @@ -37,8 +37,7 @@ intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (z <- curve_table(intervalsdf[[1]], format = "data.frame")) (z <- curve_table(intervalsdf[[1]], format = "tibble")) -(z <- curve_table(intervalsdf[[1]], format = "docx")) -(z <- curve_table(intervalsdf[[1]], format = "pptx")) (z <- curve_table(intervalsdf[[1]], format = "latex")) -(z <- curve_table(intervalsdf[[1]], format = "image")) + + } diff --git a/man/defunct.Rd b/man/defunct.Rd index 016d3e2..223049e 100644 --- a/man/defunct.Rd +++ b/man/defunct.Rd @@ -32,12 +32,12 @@ The functions listed below are deprecated. Please use the listed alternatives.} \section{\code{plotpint}}{ -For \code{plotpint}, use \code{\link{ggconcurve}} or \code{\link{plot_concurve}}. +For \code{plotpint}, use \code{\link{ggconcurve}}. } \section{\code{plotsint}}{ -For \code{plotsint}, use \code{\link{ggconcurve}} or \code{\link{plot_concurve}}. +For \code{plotsint}, use \code{\link{ggconcurve}}. } \section{\code{meanintervals}}{ diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index 24751a7..e234906 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -49,7 +49,7 @@ should be plotted. By default, it is set to FALSE, meaning it will not be plotted as a vertical line. Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " default" and a vertical line at 1 when the measure is set to "ratio". For example, -ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions.} \item{position}{Determines the orientation of the P-value (consonance) function. @@ -61,31 +61,31 @@ ggconcurve(type = "c", data = df, position = "inverted").} \item{title}{A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "consonance", +be in quotes. For example, ggconcurve(type = "c", data = x, title = "Custom Title").} \item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").} +For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").} \item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. In order to set a x-axis title, it must be in quotes. For example, -ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} +ggconcurve(type = "c", data = x, xaxis = "Hazard Ratio").} \item{yaxis}{A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} +ggconcurve(type = "c", data = x, yxis = "Confidence Level").} \item{color}{Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. -For example, ggconcurve(type = "consonance", data = x, color = "#333333").} +For example, ggconcurve(type = "c", data = x, color = "#333333").} \item{fill}{Item that allows the user to choose the color of the ribbons in the graph. -By default, it is set to color = "#239a98". The inputs must be in quotes. For example, +By default, it is set to fill = "#239a98". The inputs must be in quotes. For example, ggconcurve(type = "c", data = x, fill = "#333333").} } \value{ @@ -99,6 +99,7 @@ the interval estimates to produce a p-value/s-value function using ggplot2 graphics. } \examples{ + # Simulate random data library(concurve) @@ -110,4 +111,5 @@ RandomData <- data.frame(GroupA, GroupB) intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (function1 <- ggconcurve(type = "c", intervalsdf[[1]])) + } diff --git a/man/pipe.Rd b/man/pipe.Rd deleted file mode 100644 index 0eec752..0000000 --- a/man/pipe.Rd +++ /dev/null @@ -1,12 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/utils-pipe.R -\name{\%>\%} -\alias{\%>\%} -\title{Pipe operator} -\usage{ -lhs \%>\% rhs -} -\description{ -See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. -} -\keyword{internal} diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd index 52b1ddb..c058ad5 100644 --- a/man/plot_compare.Rd +++ b/man/plot_compare.Rd @@ -47,7 +47,7 @@ should be plotted. By default, it is set to FALSE, meaning it will not be plotted as a vertical line. Changing this to TRUE, will plot a vertical line at 0 when the measure is set to " default" and a vertical line at 1 when the measure is set to "ratio". For example, -ggconcurve(type = "consonance", data = df, measure = "ratio", nullvalue = "present"). +ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). This feature is not yet available for surprisal functions.} \item{position}{Determines the orientation of the P-value (consonance) function. @@ -59,13 +59,13 @@ ggconcurve(type = "c", data = df, position = "inverted").} \item{title}{A custom title for the graph. By default, it is set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "consonance", +be in quotes. For example, ggconcurve(type = "c", data = x, title = "Custom Title").} \item{subtitle}{A custom subtitle for the graph. By default, it is set to "The function contains consonance/confidence intervals at every level and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "consonance", data = x, subtitle = "Custom Subtitle").} +For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").} \item{xaxis}{A custom x-axis title for the graph. By default, it is set to "Range of Values. @@ -75,16 +75,20 @@ ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} \item{yaxis}{A custom y-axis title for the graph. By default, it is set to "Consonance Level". In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "consonance", data = x, yxis = "Confidence Level").} +ggconcurve(type = "c", data = x, yxis = "Confidence Level").} \item{color}{Item that allows the user to choose the color of the points and the ribbons in the graph. By default, it is set to color = "#555555". The inputs must be in quotes. -For example, ggconcurve(type = "consonance", data = x, color = "#333333").} +For example, ggconcurve(type = "c", data = x, color = "#333333").} -\item{fill}{Item that allows the user to choose the color of the ribbons in the graph. -By default, it is set to color = "#239a98". The inputs must be in quotes. For example, -ggconcurve(type = "consonance", data = x, fill = "#333333").} +\item{fill1}{Item that allows the user to choose the color of the ribbons in the graph +for data1. By default, it is set to fill1 = "#239a98". The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, fill1 = "#333333").} + +\item{fill2}{Item that allows the user to choose the color of the ribbons in the graph +for data1. By default, it is set to fill2 = "#d46c5b". The inputs must be in quotes. +For example, ggconcurve(type = "c", data = x, fill2 = "#333333").} } \value{ A plot that compares two functions. @@ -93,12 +97,13 @@ A plot that compares two functions. Compares the p-value/s-value, and likelihood functions using ggplot2 graphics. } \examples{ +\donttest{ library(concurve) GroupA <- rnorm(500) GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) @@ -106,5 +111,7 @@ model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) -(plot_compare(data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", measure = "default", nullvalue = FALSE)) +(plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) +} + } diff --git a/tests/spelling.R b/tests/spelling.R index 6713838..13f77d9 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,3 +1,6 @@ -if(requireNamespace('spelling', quietly = TRUE)) - spelling::spell_check_test(vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE) +if (requireNamespace("spelling", quietly = TRUE)) { + spelling::spell_check_test( + vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE + ) +} diff --git a/tests/testthat/testdfstructure.R b/tests/testthat/testdfstructure.R index 2983b31..c3060d9 100644 --- a/tests/testthat/testdfstructure.R +++ b/tests/testthat/testdfstructure.R @@ -1,14 +1,17 @@ + + context("Dataframe Structure") test_that("curve_mean", { library(concurve) + # Produce random sample data GroupA <- runif(100, min = 0, max = 100) GroupB <- runif(100, min = 0, max = 100) RandomData <- data.frame(GroupA, GroupB) - bob <- curve_mean(GroupA, GroupB, RandomData) + bob <- curve_mean(GroupA, GroupB, RandomData, method = "default") # Set sample dataframe. variable1 <- rnorm(100) @@ -16,19 +19,23 @@ test_that("curve_mean", { variable3 <- rnorm(100) variable4 <- rnorm(100) variable5 <- rnorm(100) + variable6 <- rnorm(100) + variable7 <- rnorm(100) - sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5) + sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7) - columnnames <- c("lower.limit", "upper.limit", "intrvl.level", "pvalue", "svalue") + columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue") colnames(sampledf) <- columnnames - expect_equivalent(str(bob), str(sampledf)) + expect_equivalent(str(bob[[1]]), str(sampledf)) }) test_that("curve_gen", { library(concurve) + + # Produce random sample data GroupA <- rnorm(50) GroupB <- rnorm(50) @@ -36,7 +43,7 @@ test_that("curve_gen", { RandomData <- data.frame(GroupA, GroupB) rob <- glm(GroupA ~ GroupB, data = RandomData) - bob <- curve_gen(rob, "GroupB", method = "lm") + bob <- curve_gen(rob, "GroupB", method = "glm") # Set sample dataframe. variable1 <- rnorm(100) @@ -44,20 +51,23 @@ test_that("curve_gen", { variable3 <- rnorm(100) variable4 <- rnorm(100) variable5 <- rnorm(100) + variable6 <- rnorm(100) + variable7 <- rnorm(100) - sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5) + sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7) - columnnames <- c("lower.limit", "upper.limit", "intrvl.level", "pvalue", "svalue") + columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue") colnames(sampledf) <- columnnames - expect_equivalent(str(bob), str(sampledf)) + expect_equivalent(str(bob[[1]]), str(sampledf)) }) test_that("curve_meta", { library(concurve) library(metafor) + # Produce random sample data GroupAData <- runif(20, min = 0, max = 100) GroupAMean <- round(mean(GroupAData), digits = 2) @@ -110,12 +120,14 @@ test_that("curve_meta", { variable3 <- rnorm(100) variable4 <- rnorm(100) variable5 <- rnorm(100) + variable6 <- rnorm(100) + variable7 <- rnorm(100) - sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5) + sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7) - columnnames <- c("lower.limit", "upper.limit", "intrvl.level", "pvalue", "svalue") + columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue") colnames(sampledf) <- columnnames - expect_equivalent(str(metaf), str(sampledf)) + expect_equivalent(str(metaf[[1]]), str(sampledf)) }) diff --git a/usethis.R b/usethis.R index 4695718..dea6428 100644 --- a/usethis.R +++ b/usethis.R @@ -30,3 +30,4 @@ use_build_ignore("usethis.R", escape = TRUE) use_spell_check(vignettes = TRUE, lang = "en-US", error = FALSE) use_cran_comments(open = interactive()) use_tidy_style() +use_revdep() From 3430b8a15b47c437a87b57f7560586595aa73f0d Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Tue, 3 Dec 2019 09:06:08 -0500 Subject: [PATCH 17/21] usethis and devtools to check for errors --- DESCRIPTION | 2 +- NEWS.md | 2 +- codemeta.json | 4 ++-- man/curve_compare.Rd | 1 + man/curve_corr.Rd | 1 - man/curve_lik.Rd | 1 - man/curve_mean.Rd | 1 - man/curve_meta.Rd | 1 - man/curve_rev.Rd | 1 - man/curve_table.Rd | 2 -- man/ggconcurve.Rd | 1 - tests/spelling.R | 9 +++------ usethis.R | 6 ++++++ 13 files changed, 14 insertions(+), 18 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2548bdc..18dad8c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,7 +3,7 @@ Package: concurve Title: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions -Version: 2.2.0 +Version: 2.3.0 Date: 2019-12-01 Authors@R: c(person(given = "Zad R.", diff --git a/NEWS.md b/NEWS.md index 655d10f..0fceb9d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# concurve 3.0 +# concurve 2.3.0 ## Major changes diff --git a/codemeta.json b/codemeta.json index 42247b6..1769096 100644 --- a/codemeta.json +++ b/codemeta.json @@ -16,7 +16,7 @@ ], "issueTracker": "https://github.com/Zadchow/concurve/issues", "license": "https://spdx.org/licenses/GPL-3.0", - "version": "2.2.0", + "version": "2.3.0", "programmingLanguage": { "@type": "ComputerLanguage", "name": "R", @@ -345,7 +345,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3837.019KB", + "fileSize": "3837.025KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd index bed8d4d..0270d85 100644 --- a/man/curve_compare.Rd +++ b/man/curve_compare.Rd @@ -43,4 +43,5 @@ randomframe <- curve_gen(model, "GroupB2", method = "glm") (curve_compare(intervalsdf[[1]], randomframe[[1]])) (curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) } + } diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd index c3f9e47..bbe100f 100644 --- a/man/curve_corr.Rd +++ b/man/curve_corr.Rd @@ -44,5 +44,4 @@ GroupA <- rnorm(50) GroupB <- rnorm(50) joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") tibble::tibble(joe[[1]]) - } diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd index aa9f973..2c37e48 100644 --- a/man/curve_lik.Rd +++ b/man/curve_lik.Rd @@ -26,5 +26,4 @@ data(dataglm) xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit")) lik <- curve_lik(xx, dataglm) tibble::tibble(lik[[1]]) - } diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd index 96bd0d8..a51a5da 100644 --- a/man/curve_mean.Rd +++ b/man/curve_mean.Rd @@ -59,5 +59,4 @@ GroupB <- runif(100, min = 0, max = 100) RandomData <- data.frame(GroupA, GroupB) bob <- curve_mean(GroupA, GroupB, RandomData) tibble::tibble(bob[[1]]) - } diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd index 096f8ad..b6e81a5 100644 --- a/man/curve_meta.Rd +++ b/man/curve_meta.Rd @@ -79,5 +79,4 @@ res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE metaf <- curve_meta(res) # tibble::tibble(metaf[[1]]) - } diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd index fbfbbf6..6ba3cc0 100644 --- a/man/curve_rev.Rd +++ b/man/curve_rev.Rd @@ -52,5 +52,4 @@ intervals to form a consonance and surprisal function. df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") tibble::tibble(df[[1]]) - } diff --git a/man/curve_table.Rd b/man/curve_table.Rd index 1e565d0..0d0014a 100644 --- a/man/curve_table.Rd +++ b/man/curve_table.Rd @@ -38,6 +38,4 @@ intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (z <- curve_table(intervalsdf[[1]], format = "data.frame")) (z <- curve_table(intervalsdf[[1]], format = "tibble")) (z <- curve_table(intervalsdf[[1]], format = "latex")) - - } diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd index e234906..b64095c 100644 --- a/man/ggconcurve.Rd +++ b/man/ggconcurve.Rd @@ -111,5 +111,4 @@ RandomData <- data.frame(GroupA, GroupB) intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") (function1 <- ggconcurve(type = "c", intervalsdf[[1]])) - } diff --git a/tests/spelling.R b/tests/spelling.R index 13f77d9..6713838 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,6 +1,3 @@ -if (requireNamespace("spelling", quietly = TRUE)) { - spelling::spell_check_test( - vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE - ) -} +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE) diff --git a/usethis.R b/usethis.R index dea6428..3776b7f 100644 --- a/usethis.R +++ b/usethis.R @@ -31,3 +31,9 @@ use_spell_check(vignettes = TRUE, lang = "en-US", error = FALSE) use_cran_comments(open = interactive()) use_tidy_style() use_revdep() + +check(pkg = ".", document = NA, build_args = NULL, + manual = TRUE, cran = TRUE, remote = TRUE, incoming = TRUE, + force_suggests = TRUE, run_dont_test = TRUE, args = "--timings", + env_vars = NULL, quiet = FALSE, check_dir = 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-[![Travis build -status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) -[![Lifecycle: -maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) -[![](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran.r-project.org/package=concurve) -[![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve) -[![License: GPL -v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) - - - ------ - -# Examples - ------ - -## Plot Publication-Ready Interval Functions - -### (Consonance, Surprisal, Likelihood, & Deviance) - - - - - - ------ - -## Compare Interval Functions From Different Data/Studies - - - - - - ------ - -## Export Tabled Results Easily For Word, Powerpoint, & LaTeX - - - -# Installation - -## For R: - -### Install the Package From CRAN - -``` r -install.packages("concurve") -``` - -### Install the Developer Version - -``` r -library(devtools) -install_github("zadchow/concurve") -``` - -### Check out the [Examples](https://data.lesslikely.com/concurve/articles/examples.html). - ------ - -## For Stata: - -### Check out the [Article on Using Stata](https://data.lesslikely.com/concurve/articles/stata.html) for concurve. - ------ - -# Dependencies - - - ggplot2 - - metafor - - parallel - - MASS - - boot - - bcaboot - - compiler - - ProfileLikelihood - - pbmcapply - - rlang - - magrittr - - dplyr - - tidyr - - knitr - - flextable - - officer - - tibble - - survival - - survminer - - scales - ------ - -# Purpose - -> In particular, the usual 95% default forces the user’s focus onto -> parameter values that yield p \> 0.05, without regard to the trivial -> difference between (say) p = 0.06 and p = 0.04 (a difference not even -> worth a coin toss). To address this problem, we first note that a 95% -> interval estimate is only one of a number of arbitrary dichotomization -> of possibilities of parameter values (into either inside or outside of -> an interval). A more accurate picture of uncertainty is then obtained -> by examining intervals using other percentiles, e.g., -> proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, -> which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s -> \< 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or -> graph of all the P-values and S-values across a broad range of -> parameter values seems the clearest way to see how compatibility -> varies smoothly across the values. -> -> Graphs of P-values or their equivalent have been promoted for decades -> \[34, 56–59\], yet their adoption has been slight. Nonetheless, -> P-value and S-value graphing software is now available freely through -> several statistical packages \[60–62\]. A graph of the P-values p -> against possible parameter values allows one to see at a glance which -> parameter values are most compatible with the data under the -> background assumptions. This graph is known as the P-value function, -> or compatibility, consonance, or confidence curve \[34, 57, 58, 63, -> 64\]. Transforming the corresponding P-values in the graph to S-values -> produces an S-value (surprisal) function. -> -> Following the common (and important) warning that P-values are not -> hypothesis probabilities, we caution that the P-value graph is not a -> probability distribution: It shows compatibility of parameter values -> with the data, rather than plausibility or probability of those values -> given the data. This is not a subtle difference: compatibility is a -> much weaker condition than plausibility. Consider for example that -> complete fabrication of the data is always an explanation compatible -> with the data (and indeed has happened in some influential medical -> studies \[65\]), but in studies with many participants and authors -> involved in all aspects of data collection it becomes so implausible -> or improbable as to not even merit mention. We emphasize then that all -> the P-value ever addresses in a direct logical sense is compatibility; -> for hypothesis probabilities one must turn to Bayesian methods \[25\]. -> - [Chow & Greenland, 2019](https://arxiv.org/abs/1909.08579); -> [Greenland & Chow, 2019](https://arxiv.org/abs/1909.08583) - ------ - -> In addition to the overt statistical position, the p-value function -> also provides easily and accurately many of the familiar types of -> summary information: a **median estimate** of the parameter; a -> **one-sided test statistic** for a scalar parameter value at any -> chosen level; the related **power function**; a **lower confidence -> bound** at any level; an **upper confidence bound** at any level; and -> **confidence intervals** with chosen upper and lower confidence -> limits. The p value reports all the **common inference material**, but -> with **high accuracy, basic uniqueness, and wide generality**. -> -> From a scientific perspective, the **likelihood function** and -> **p-value function** provide the basis for scientific judgments by an -> investigator, and by other investigators who might have interest. **It -> thus replaces a blunt yes or no decision by an opportunity for -> appropriate informed judgment.**” - -> [Fraser, 2019](https://doi.org/10.1080/00031305.2018.1556735) - ------ - -> *Statistical software does not help you know what to compute, nor how -> to interpret the result. It does not offer to explain the assumptions -> behind methods, nor does it flag delicate or dubious assumptions. It -> does not warn you about multiplicity or p-hacking. It does not check -> whether you picked the hypothesis or analysis after looking at the -> data, nor track the number of analyses you tried before arriving at -> the one you sought to publish – another form of multiplicity. The more -> “powerful” and “user-friendly” the software is, the more it invites -> cargo-cult statistics*." - Stark & Saltelli, 2018 - -# References - -1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid - Statistical Inference: Replace Confidence and Significance by - Compatibility and Surprise. [*arXiv:1909.08579 - \[stat.ME\]*.](https://arxiv.org/abs/1909.08579) 2019 -2. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize - Unconditional Descriptions of Statistics. [*arXiv:1909.08583 - \[stat.ME\]*.](https://arxiv.org/abs/1909.08583) 2019 -3. Poole C. Beyond the confidence interval. *Am J Public Health.* - 1987;77(2):195-199. -4. Sullivan KM, Foster DA. Use of the confidence interval - function.\_Epidemiology.\_ 1990;1(1):39-42. -5. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012. -6. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – - distribution estimator of a parameter. *arXiv \[mathST\]*. 2007. -7. Schweder T, Hjort NL. Confidence and Likelihood\*. *Scand J Stat.* - 2002;29(2):309-332. -8. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as - Descriptive Statistics: There is No Replication Crisis if We Don’t - Expect Replication. *Am Stat*. 2019 -9. Greenland S. Valid P-values Behave Exactly As They Should. Some - misleading criticisms of P-values and their resolution with - S-values. *Am Stat*. 2019;18(136). -10. Fraser DAS. The p-value Function and Statistical Inference. *Am - Stat*. 2019 -11. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. - *Significance.* 2018;15(4):40-43. - -# Session info - - ## R version 3.6.1 (2019-07-05) - ## Platform: x86_64-apple-darwin15.6.0 (64-bit) - ## Running under: macOS Catalina 10.15.1 - ## - ## Matrix products: default - ## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib - ## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib - ## - ## locale: - ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 - ## - ## attached base packages: - ## [1] stats graphics grDevices utils methods base - ## - ## loaded via a namespace (and not attached): - ## [1] compiler_3.6.1 magrittr_1.5 tools_3.6.1 htmltools_0.4.0 yaml_2.2.0 Rcpp_1.0.3 stringi_1.4.3 - ## [8] rmarkdown_1.18 knitr_1.26 stringr_1.4.0 xfun_0.11 digest_0.6.23 rlang_0.4.2 evaluate_0.14 diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index abb4ea6..0000000 --- a/docs/404.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -Page not found (404) • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - -
    - -

    Thanks for using concurve. Before filing an issue, there are a few places to explore and pieces to put together to make the process as smooth as possible.

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    Start by making a minimal reproducible example using the reprex package. If you haven’t heard of or used reprex before, you’re in for a treat! Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it’ll take you to learn what it’s all about). For additional reprex pointers, check out the Get help! section of the tidyverse site.

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    Armed with your reprex, the next step is to figure out where to ask.

    -
      -
    • If it’s a question: start with community.rstudio.com, and/or StackOverflow. There are more people there to answer questions.
      -
    • -
    • If it’s a bug: you’re in the right place, file an issue.
      -
    • -
    • If you’re not sure: let the community help you figure it out! If your problem is a bug or a feature request, you can easily return here and report it.
    • -
    -

    Before opening a new issue, be sure to search issues and pull requests to make sure the bug hasn’t been reported and/or already fixed in the development version. By default, the search will be pre-populated with is:issue is:open. You can edit the qualifiers (e.g. is:pr, is:closed) as needed. For example, you’d simply remove is:open to search all issues in the repo, open or closed.

    -

    If you are in the right place, and need to file an issue, please review the “File issues” paragraph from the tidyverse contributing guidelines.

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    Thanks for your help!

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    - - - - - - - - - - - diff --git a/docs/articles/examples.html b/docs/articles/examples.html deleted file mode 100644 index ca289e2..0000000 --- a/docs/articles/examples.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - - - -Examples in R • concurve - - - - - - - - - - - - - - - - - - - - -
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    -Using Mean Differences

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    If we were to generate some random data from a normal distribution with the following code,

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    and compare the means of these two “groups” using a t-test,

    -
    testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE)
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    we would likely see some differences, given that we have such a small sample in each group.

    -
    summary(testresults)
    -

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

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    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

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    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

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    It is relatively easy to produce such a function using the concurve package in R.

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    Install the package directly from CRAN

    -
    install.packages("concurve")
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    or get a more slightly up-to-date version via GitHub.

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    library(devtools)
    -install_github("zadchow/concurve")
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    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    - -

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    -
    tibble::tibble(intervalsdf)
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    There’s a quick look at the first few entries of our dataframe.

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    We can plot this data using the ggconcurve() function.

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    pfunction <- ggconcurve(type = "consonance", intervalsdf)
    -pfunction
    -
    - -
    -

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    -

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    -
    sfunction <- ggconcurve(type = "surprisal", intervalsdf)
    -sfunction
    -
    - -
    -

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    -
    -
    -

    -Simple Linear Models

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    We can also try this with other simple linear models.

    -

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    -
    GroupA2 <- rnorm(500)
    -GroupB2 <- rnorm(500)
    -
    -RandomData2 <- data.frame(GroupA2, GroupB2)
    -
    -model <- lm(GroupA2 ~ GroupB2, data = RandomData2)
    -
    -summary(model)
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    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    -
    randomframe <- curve_gen(model, "GroupB2")
    -tibble::tibble(randomframe)
    -

    Now that we have our data frame, we can graph our function.

    -
    ggconcurve(type = "consonance", randomframe)
    -
    - -
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    s <- ggconcurve(type = "surprisal", randomframe)
    -s
    -
    - -
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    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    - -

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    - -
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    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    -
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    -

    -Using Meta-Analysis Data

    -

    Here we present another quick example with a meta-analysis of simulated data.

    -

    First, we generate random data for two groups in two hypothetical studies

    -
    GroupAData <- runif(20, min = 0, max = 100)
    -GroupAMean <- round(mean(GroupAData), digits = 2)
    -GroupASD <- round(sd(GroupAData), digits = 2)
    -
    -GroupBData <- runif(20, min = 0, max = 100)
    -GroupBMean <- round(mean(GroupBData), digits = 2)
    -GroupBSD <- round(sd(GroupBData), digits = 2)
    -
    -GroupCData <- runif(20, min = 0, max = 100)
    -GroupCMean <- round(mean(GroupCData), digits = 2)
    -GroupCSD <- round(sd(GroupCData), digits = 2)
    -
    -GroupDData <- runif(20, min = 0, max = 100)
    -GroupDMean <- round(mean(GroupDData), digits = 2)
    -GroupDSD <- round(sd(GroupDData), digits = 2)
    -

    We can then quickly combine the data in a dataframe.

    -
    StudyName <- c("Study1", "Study2")
    -MeanTreatment <- c(GroupAMean, GroupCMean)
    -MeanControl <- c(GroupBMean, GroupDMean)
    -SDTreatment <- c(GroupASD, GroupCSD)
    -SDControl <- c(GroupBSD, GroupDSD)
    -NTreatment <- c(20, 20)
    -NControl <- c(20, 20)
    -
    -metadf <- data.frame(
    -  StudyName, MeanTreatment, MeanControl,
    -  SDTreatment, SDControl,
    -  NTreatment, NControl
    -)
    -

    Then, we’ll use metafor to calculate the standardized mean difference.

    - -

    Next, we’ll pool the data using a fixed-effects common-effects model

    - -

    Let’s look at our output.

    - -
    Fixed-Effects Model (k = 2)
    -
    -Test for Heterogeneity: 
    -Q(df = 1) = 0.61, p-val = 0.44
    -
    -Model Results:
    -
    -estimate    se  zval  pval  ci.lb  ci.ub 
    -    0.16  0.22  0.73  0.47  -0.28   0.60  
    -
    ----
    -Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    -

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    -

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    - -

    Now that we have our dataframe with every computed interval, we can plot the functions.

    -
    ggconcurve(type = "consonance", metaf)
    -
    - -
    -

    And our S-value function

    -
    ggconcurve(type = "surprisal", metaf)
    -
    - -
    -

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

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z;A=bwyqaw4zb00XB(FuxB@C3UbuT^#DNLA8r!=nAopA3xAUb^v{u)G$;k(E>l3`bNz!geJ0FpIgpKg^QI*pR)5~c#-mCI zc;T?HmoLyG$8lv(?|cs$fQ$X|EtaYQ?&Z-$cl)V_wu<6PHXnGJ9U4{Q^74HN<+~}r z!oGd-3QBqxEoC)Mt?E>J8Frz<7t`Gtl=p;S2KlUd-ysNsx-A^Re3CZ)Ri(V5yxnvU zQVs;_SB^9NMHn4RtGyR5T+lc-S?t%KFyTz^zxGx)j+@IADjD8MK-kr(j!fNN zsGofbSsM&re&~HPbP{~^*%($cd#ZSMJhLG`S}N(_l7F{CTH(N{h-AH)mjvBlN@tdJ z9{vGcO;$*OmNni3X;hC+CDY1li;!R1l;AbjvbxWG1`%kbBx?Uyr=%Za7#Cg*FrX9S z`od<6+?Y=74=;!w0BnnIu*wpi9)YN&L8_yq@V4>7F`~V{ALAS-xk==$==XmZf0ygl z2YJ^q3tJUCvzTi#d9@h&2{YCi`Gv>)G-{P^M&M_vLyYiluPsK??A?eiJ7^8iJy^g4 z0(uWAeLq_)?9G#eC(KZv8_U#M1G06Wxw-lKax_}ULg>0t7&e>Rj1T@^`(}v{u#{yu__FR>C5nDc!pOrIfZzU`wbcXe=WJuHJ+Fzy?)H58pTcEXKrG7vdrjG G)IR~$U_#3P diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 91881a5..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,198 +0,0 @@ - - - - - - - - -Articles • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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    - - - - - - - - - - - diff --git a/docs/articles/stata.html b/docs/articles/stata.html deleted file mode 100644 index 37c5ef4..0000000 --- a/docs/articles/stata.html +++ /dev/null @@ -1,302 +0,0 @@ - - - - - - - -Using Stata • concurve - - - - - - - - - - - - - - - - - - - - -
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    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

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    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

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    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

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    First, let’s visualize the data with a scatterplot.

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    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

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    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

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    That’s what our output looks like.

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    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

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    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

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    Here’s the code that we’ll be using to achieve that.

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    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

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    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

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    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

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    • pvalue”is computed by taking 1 - “level”, which is alpha.

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    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

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    • my_new_data” is the filename that we’ve assigned to our new dataset.

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    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

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    Here are the next few major lines

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    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

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    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

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    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

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    The next line two lines

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    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

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    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

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    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

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    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

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    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

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    “post topost” is writing the results from each loop as new observations in this data structure.

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    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

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    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

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    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

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    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

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    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

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    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

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    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

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    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

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    So now, here’s what our consonance function looks like.

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    And here’s what our surprisal function looks like.

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    It looks slighly better when the y-axis is transformed logarithmically…

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    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

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    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

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    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

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    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

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    We simply replace the first line within the loop with our intended command, just as I’ve replaced

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    with

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    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

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    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

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    - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 660c3b9..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,218 +0,0 @@ - - - - - - - - -Citation and Authors • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    - -
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    - - -

    Chow Z, Vigotsky A (2019). -concurve: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions. -https://CRAN.R-project.org/package=concurve. -

    -
    @Manual{,
    -  title = {{concurve}: Computes and Plots Compatibility (Confidence) Intervals, P-Values, S-Values, & Likelihood Intervals to Form Consonance, Surprisal, & Likelihood Functions},
    -  author = {Zad R. Chow and Andrew D. Vigotsky},
    -  year = {2019},
    -  url = {https://CRAN.R-project.org/package=concurve},
    -}
    - - - - - -
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    - - - - - - - - - - - diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; 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- var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/docsearch.json b/docs/docsearch.json deleted file mode 100644 index aa1fc65..0000000 --- a/docs/docsearch.json +++ /dev/null @@ -1,95 +0,0 @@ -{ - "index_name": "lesslikely-concurve", - "start_urls": [ - { - "url": "https://data.lesslikely.com/concurve//index.html", - "selectors_key": "homepage", - "tags": [ - "homepage" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//reference", - "selectors_key": "reference", - "tags": [ - "reference" - ] - }, - { - "url": "https://data.lesslikely.com/concurve//articles", - "selectors_key": "articles", - "tags": [ - "articles" - ] - } - ], - "stop_urls": [ - "/reference/$", - "/reference/index.html", - "/articles/$", - "/articles/index.html" - ], - "sitemap_urls": [ - "https://data.lesslikely.com/concurve//sitemap.xml" - ], - "selectors": { - "homepage": { - "lvl0": { - "selector": ".contents h1", - "default_value": "concurve Home page" - }, - "lvl1": { - "selector": ".contents h2" - }, - "lvl2": { - "selector": ".contents h3", - "default_value": "Context" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li, .contents .pre" - }, - "reference": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name", - "default_value": "Argument" - }, - "lvl2": { - "selector": ".ref-arguments th", - "default_value": "Description" - }, - "lvl3": ".ref-arguments td, .ref-description", - "text": ".contents p, .contents li" - }, - "articles": { - "lvl0": { - "selector": ".contents h1" - }, - "lvl1": { - "selector": ".contents .name" - }, - "lvl2": { - "selector": ".contents h2, .contents h3", - "default_value": "Context" - }, - "text": ".contents p, .contents li, .tempate-article .contents .pre" - } - }, - "selectors_exclude": [ - ".dont-index" - ], - "min_indexed_level": 2, - "custom_settings": { - "separatorsToIndex": "_", - "attributesToRetrieve": [ - "hierarchy", - "content", - "anchor", - "url", - "url_without_anchor" - ] - } -} - diff --git a/docs/extra.css b/docs/extra.css deleted file mode 100644 index 0f1be09..0000000 --- a/docs/extra.css +++ /dev/null @@ -1,98 +0,0 @@ -html, body { - height: 100%; 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- color: #bbb; - display: inline; - padding-right: 0.5em; -} - -pre code { - font-size: 13.5px; - white-space: pre; -} - -.btn-primary { - color: #eeeeee; - background-color: #d46c5b; -} - -.btn-primary:focus { - color: #eeeeee; - background-color: #d46c5b; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index b8d7886..0000000 --- a/docs/index.html +++ /dev/null @@ -1,365 +0,0 @@ - - - - - - - -Computes and Plots Compatibility (Confidence) Intervals, - P-Values, S-Values, & Likelihood Intervals to Form Consonance, - Surprisal, & Likelihood Functions • concurve - - - - - - - - - - - - - - - - - - - - -
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    -Examples

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    -Plot Publication-Ready Interval Functions

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    -(Consonance, Surprisal, Likelihood, & Deviance)

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    -Compare Interval Functions From Different Data/Studies

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    -Export Tabled Results Easily For Word, Powerpoint, & LaTeX

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    -Installation

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    -For R:

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    -Install the Package From CRAN

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    install.packages("concurve")
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    -Install the Developer Version

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    library(devtools)
    -install_github("zadchow/concurve")
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    -Check out the Examples.

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    -For Stata:

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    -Check out the Article on Using Stata for concurve.

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    -Dependencies

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    • ggplot2
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    • metafor
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    • parallel
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    • MASS
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    • boot
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    • bcaboot
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    • compiler
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    • ProfileLikelihood
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    • pbmcapply
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    • rlang
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    • magrittr
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    • dplyr
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    • tidyr
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    • knitr
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    -Purpose

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    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

    -

    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

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    Following the common (and important) warning that P-values are not hypothesis probabilities, we caution that the P-value graph is not a probability distribution: It shows compatibility of parameter values with the data, rather than plausibility or probability of those values given the data. This is not a subtle difference: compatibility is a much weaker condition than plausibility. Consider for example that complete fabrication of the data is always an explanation compatible with the data (and indeed has happened in some influential medical studies [65]), but in studies with many participants and authors involved in all aspects of data collection it becomes so implausible or improbable as to not even merit mention. We emphasize then that all the P-value ever addresses in a direct logical sense is compatibility; for hypothesis probabilities one must turn to Bayesian methods [25].

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    In addition to the overt statistical position, the p-value function also provides easily and accurately many of the familiar types of summary information: a median estimate of the parameter; a one-sided test statistic for a scalar parameter value at any chosen level; the related power function; a lower confidence bound at any level; an upper confidence bound at any level; and confidence intervals with chosen upper and lower confidence limits. The p value reports all the common inference material, but with high accuracy, basic uniqueness, and wide generality.

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    From a scientific perspective, the likelihood function and p-value function provide the basis for scientific judgments by an investigator, and by other investigators who might have interest. It thus replaces a blunt yes or no decision by an opportunity for appropriate informed judgment.” - Fraser, 2019

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    Statistical software does not help you know what to compute, nor how to interpret the result. It does not offer to explain the assumptions behind methods, nor does it flag delicate or dubious assumptions. It does not warn you about multiplicity or p-hacking. It does not check whether you picked the hypothesis or analysis after looking at the data, nor track the number of analyses you tried before arriving at the one you sought to publish – another form of multiplicity. The more “powerful” and “user-friendly” the software is, the more it invites cargo-cult statistics." - Stark & Saltelli, 2018

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    -References

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    1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:1909.08579 [stat.ME]. 2019
    2. -
    3. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics. arXiv:1909.08583 [stat.ME]. 2019
    4. -
    5. Poole C. Beyond the confidence interval. Am J Public Health. 1987;77(2):195-199.
    6. -
    7. Sullivan KM, Foster DA. Use of the confidence interval function._Epidemiology._ 1990;1(1):39-42.
    8. -
    9. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012.
    10. -
    11. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. arXiv [mathST]. 2007.
    12. -
    13. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332.
    14. -
    15. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There is No Replication Crisis if We Don’t Expect Replication. Am Stat. 2019
    16. -
    17. Greenland S. Valid P-values Behave Exactly As They Should. Some misleading criticisms of P-values and their resolution with S-values. Am Stat. 2019;18(136).
    18. -
    19. Fraser DAS. The p-value Function and Statistical Inference. Am Stat. 2019
    20. -
    21. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15(4):40-43.
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    - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/logo.svg b/docs/logo.svg deleted file mode 100644 index f42ed9b..0000000 --- a/docs/logo.svg +++ /dev/null @@ -1,93 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index 9f0dbf3..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,330 +0,0 @@ - - - - - - - - -Changelog • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    -concurve 3.0 Unreleased -

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    -Major changes

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    • -ggconcurve() plots confidence (consonance) distributions, densities, and likelihood functions.
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    • -curve_compare() compares two functions and calculates the area between the curve.
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    • -curve_table() produces publication-ready tables of relevant statistics.
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    • All functions now provide progress on how long it will take to complete the task.
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    -concurve 2.1.0 2019-09-19 -

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    • -ggconcurve() now plots both the P-values and CI level using both y-axes when the type = “consonance”. Previously, this was only possible via plot_concurve() (which uses base R graphics) because ggplot2 had a bug in its last few versions, which inhibited proper transformations in the y-axis.
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    -concurve 2.0.1 Unreleased -

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    • -plot_concurve() now has “measure” as an item which allows for ratio measures to be logarithmically scaled on the x axis. There are two options, “default”, which is set as the default option and is for mean differences, and “ratio”, which will result in the axis being logarithmically scaled.
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    -concurve 2.0 2019-07-10 -

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    • Functions can now also be plotted with base R via the plot_concurve() function.
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    -concurve 1.08 Unreleased -

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    -concurve 1.06 Unreleased -

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    - - - - - - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 9145958..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,256 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; 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- - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('body').scrollspy({ - target: '#sidebar', - offset: 60 - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
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    All vignettes

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  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index c4bf0d4..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,10 +0,0 @@ -pandoc: '2.6' -pkgdown: 1.4.1 -pkgdown_sha: ~ -articles: - examples: examples.html - stata: stata.html -urls: - reference: https://data.lesslikely.com/concurve//reference - article: https://data.lesslikely.com/concurve//articles - diff --git a/docs/reference/concurve-package.html b/docs/reference/concurve-package.html deleted file mode 100644 index b0cf55e..0000000 --- a/docs/reference/concurve-package.html +++ /dev/null @@ -1,259 +0,0 @@ - - - - - - - - -concurve: Computes and Plots Compatibility (Confidence) Intervals, - P-Values, S-Values, & Likelihood Intervals to Form Consonance, - Surprisal, & Likelihood Functions — concurve-package • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    - - -
    -

    logo

    -

    Allows one to compute consonance (confidence) - intervals for various statistical tests along with their corresponding - P-values, S-values, and likelihoods. The intervals can be plotted to - create consonance, surprisal, and likelihood functions allowing one to - see what effect sizes are compatible with the test model at various - consonance levels rather than being limited to one interval estimate - such as 95 - <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) - <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) - <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2008, - ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019) - <doi:10.1080/00031305.2018.1543137>, Greenland S. (2019) - <doi:10.1080/00031305.2018.1529625>, Chow ZR, Greenland S. (2019) - <arXiv:1909.08579>, and Greenland S, Chow ZR. (2019) - <arXiv:1909.08583>.

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    See also

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    - - - - - - - - - - - diff --git a/docs/reference/curve_boot-1.png b/docs/reference/curve_boot-1.png deleted file mode 100644 index 16f95c4cd15b36aabb77ce35b6fb1a6aa59b8cef..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119640 zcmeGEcQl+`+XoDXkOUDSK|+*74MGq-h!TPz5^dB(XY}4h3nGH(y+xfFW%M3h^eChE zZuH*0+w(m4eP6k*-2Z;-TkC!QkTq*&GEV2-`#65>n1DBOlK8k}xL2-R!Iyd^{`Sfh zY{x5CuIk*l2A&y>Oxe701?#@CnAjVrn53A6sfG1B%MZHxlKSTQ*2cPTB}K1X;SCB> z(lmOYbXTa-#*l`#>z(N{U)wBUd^-Fvc^!C@gqJMVra#o(D z$iIB!%DxhJx3RYLBQ@h5!KhFXTLdoMNIx~BP|>0Ho29@Ebc_IJjB!Q5g#NngPQpf_ z%X+!t2KG{Uj%sA+T?~@g+$W?QGd6y~{({UQK7Prh*HTmzD3~>~Ut@C1<2e+oPm0yM zXb%sJpC=(oB`Aogt*CSg7qamDgE2NUmD=qwoR34Pgjduk_jQ&^*H+nTu|5TA@)U=UbA*>5UnvnOn(&snN)|P(tBrA!8Gp3+^-x0;1AG$C4aPP3pJn`@5S!{b(Hw4QGYl^S4Z!G0f+ujzy`Vml}9=p|IePZ@5 zaeHxog)6q*#Yz11xXS6(zMFLt-`cY4(vDB$lk2DHQQ_Xijo$tqDH0<4i!ZC2APW2sx_CMi!pl?(~`*z;}jg?M40^~XHOHJHMeAo-Ce4SpdGHe>WtziM1r)vJVhj4!TDk_;JTd*8aoaTjs-{%7qJ15)zE 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zU2T35TJydU;lxaB@y5-+w{MH3?c{wu^1;dc5+BohS(UWBkh)5CPS?9r$_18ac;6dQ za9F?KO)R)Pek*hbX)b%)qXcbe&S|GioHK_z{W)cS{Nr5yil#mk@s=S4&vN)E|0Yyr zsGE~mHB=gZA0trw@MCuXNU?pzp=Q~*% z@sK{SokRZubl$B;V*12L(k}X7ld?<#T4Rxr=Rf5Lhs;OkqdE_Zz`#0HCC%NXJ&}AugpHa(={HyMOVw&JoETt z<2#s$)PGO_`_*2f7Uq3BE7`Hz1Ny|(N-p|r0bHvLwg>^MqN6SRwP;t)iVX(xWi>cn zI{HdE>x6^xcDglW%?-no`{YgNACs65xD>^r*dpQ2o%4E;3A+{tNnpJccvKy$X zsq>zl8Klx77Fa@tOplcH45#M63rI8op;Vz(TC`!Ne zehiLS7@O`iHcgG}&K>d!XH~CMT%Z&pF{?X!om*1#h)%PTB0q#mG_u^Tayp$^Rt?8m z@JN-fnp*A>=aj0DxAXV0!OI+rWMM6TMmxx!eSf?0adc6rp~;k^l7dSGdy4&T0l*^9iLZNA&i06 zhOpNC4VD=xYd8F!e+`!MFxFKH0(~O=@Z928NTG_tbVvlYE_C1gTmBVA7|9}{+<}pb zkPHAa1rmY#Pz2xEtb&bZqe=svEn)`j#2$KAABA&Kt;-BxsJ2-5ufpkB;L`NQ?W4f~ eOD=;aIR42uiK{iWd1zNL0D-5gpUXO@geCxmjTwyq diff --git a/docs/reference/curve_compare.html b/docs/reference/curve_compare.html deleted file mode 100644 index 5883c96..0000000 --- a/docs/reference/curve_compare.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Compares two functions and produces an AUC score to show the amount of consonance. — curve_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

    -
    - - - - -
    - -
    -
    - - -
    -

    Compares the p-value/s-value, and likelihood functions and computes an AUC number.

    -
    - -
    curve_compare(data1, data2, type = "c", plot = TRUE, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    data1

    The first dataframe produced by one of the interval functions -in which the intervals are stored.

    data2

    The second dataframe produced by one of the interval functions in -which the intervals are stored.

    type

    Choose whether to plot a "consonance" function, a "surprisal" function or -"likelihood". The default option is set to "c". The type must be set in quotes, -for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options -include "pd" for the consonance distribution function, and "cd" for the consonance -density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" -for likelihood and "d" for deviance function.

    plot

    by default it is set to TRUE and will use the plot_compare() function -to plot the two functions.

    ...

    Can be used to pass further arguments to plot_compare().

    - - -

    Examples

    -
    # \donttest{ -library(concurve) -GroupA <- rnorm(500) -GroupB <- rnorm(500) -RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) -RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2", method = "glm") -(curve_compare(intervalsdf[[1]], randomframe[[1]]))
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] -#> -#> -#> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| -#> |-----:|-----:|----------:|---------------:|-----------------------------:| -#> | 0.102| 0.072| 0.023| 0.151| 0.178| -#> -#> [[2]]
    #>
    (curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))
    #> [1] "AUC = Area Under the Curve"
    #> [[1]] -#> -#> -#> | AUC 1| AUC 2| Shared AUC| AUC Overlap (%)| Overlap:Non-Overlap AUC Ratio| -#> |-----:|-----:|----------:|---------------:|-----------------------------:| -#> | 4.115| 1.522| 1.522| 0.37| 0.587| -#> -#> [[2]]
    #>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_corr.html b/docs/reference/curve_corr.html deleted file mode 100644 index a6665c2..0000000 --- a/docs/reference/curve_corr.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - - -Computes Consonance Intervals for Correlations — curve_corr • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes consonance intervals to produce P- and S-value functions for -correlational analysesusing the cor.test function in base R and places the -interval limits for each interval levelinto a data frame along with the -corresponding p-values and s-values.

    -
    - -
    curve_corr(x, y, alternative, method, steps = 10000, table = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    A vector that contains the data for one of the variables that will -be analyzed for correlational analysis.

    y

    A vector that contains the data for one of the variables that will -be analyzed for correlational analysis.

    alternative

    Indicates the alternative hypothesis and must be one of "two.sided", -"greater" or "less". You can specify just the initial letter. "greater" corresponds to -positive association, "less" to negative association.

    method

    A character string indicating which correlation coefficient is -to be used for the test. One of "pearson", "kendall", or "spearman", -can be abbreviated.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    # \donttest{ -GroupA <- rnorm(50) -GroupB <- rnorm(50) -joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") -tibble::tibble(joe[[1]])
    #> # A tibble: 10,000 x 1 -#> `joe[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.143 0.143 0 0 0.5 1 -#> 2 0.143 0.143 0.0000358 0.0001 0.500 1.000 -#> 3 0.143 0.143 0.0000716 0.0002 0.500 1.000 -#> 4 0.143 0.143 0.000107 0.000300 0.500 1.000 -#> 5 0.143 0.143 0.000143 0.0004 0.500 1.000 -#> 6 0.143 0.143 0.000179 0.0005 0.500 1.000 -#> 7 0.143 0.143 0.000215 0.000600 0.500 0.999 -#> 8 0.143 0.143 0.000251 0.0007 0.500 0.999 -#> 9 0.143 0.143 0.000287 0.0008 0.500 0.999 -#> 10 0.143 0.143 0.000322 0.0009 0.500 0.999 -#> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_gen.html b/docs/reference/curve_gen.html deleted file mode 100644 index 501baa5..0000000 --- a/docs/reference/curve_gen.html +++ /dev/null @@ -1,283 +0,0 @@ - - - - - - - - -General Consonance Functions Using Profile Likelihood, Wald, -or the bootstrap method for linear models. — curve_gen • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for -the chosen parameter in the selected model -(ANOVA, ANCOVA, regression, logistic regression) and places -the interval limits for each interval level into a data frame along -with the corresponding p-values and s-values.

    -
    - -
    curve_gen(model, var, method = "wald", steps = 1000, table = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    model

    The statistical model of interest -(ANOVA, regression, logistic regression) is to be indicated here.

    var

    The variable of interest from the model (coefficients, intercept) -for which the intervals are to be produced.

    method

    Chooses the method to be used to calculate the -consonance intervals. There are currently four methods: -"default", "wald", "lm", and "boot". The "default" method uses the profile -likelihood method to compute intervals and can be used for models created by -the 'lm' function. The "wald" method is typicallywhat most people are -familiar with when computing intervals based on the calculated standard error. -The "lm" method allows this function to be used for specific scenarios like -logistic regression and the 'glm' function. The "boot" method allows for -bootstrapping at certain levels.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    # \donttest{ -# Simulate random data -GroupA <- rnorm(50) -GroupB <- rnorm(50) -RandomData <- data.frame(GroupA, GroupB) -rob <- glm(GroupA ~ GroupB, data = RandomData) -bob <- curve_gen(rob, "GroupB", method = "glm") -tibble::tibble(bob[[1]])
    #> # A tibble: 998 x 1 -#> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.0876 -0.0872 0.000396 0.001 0.500 0.999 -#> 2 -0.0878 -0.0870 0.000793 0.002 0.501 0.998 -#> 3 -0.0880 -0.0868 0.00119 0.003 0.501 0.997 -#> 4 -0.0882 -0.0866 0.00159 0.004 0.502 0.996 -#> 5 -0.0884 -0.0864 0.00198 0.005 0.502 0.995 -#> 6 -0.0886 -0.0862 0.00238 0.006 0.503 0.994 -#> 7 -0.0887 -0.0860 0.00277 0.007 0.503 0.993 -#> 8 -0.0889 -0.0858 0.00317 0.008 0.504 0.992 -#> 9 -0.0891 -0.0856 0.00357 0.009 0.504 0.991 -#> 10 -0.0893 -0.0854 0.00396 0.01 0.505 0.99 -#> # … with 988 more rows, and 1 more variable: $svalue <dbl>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_lik.html b/docs/reference/curve_lik.html deleted file mode 100644 index 36b10fe..0000000 --- a/docs/reference/curve_lik.html +++ /dev/null @@ -1,244 +0,0 @@ - - - - - - - - -Compute the Profile Likelihood Functions — curve_lik • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Compute the Profile Likelihood Functions

    -
    - -
    curve_lik(likobject, data, table = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    likobject

    An object from the ProfileLikelihood package

    data

    The dataframe that was used to create the likelihood -object in the ProfileLikelihood package.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    library(ProfileLikelihood)
    #> Loading required package: nlme
    #> Loading required package: MASS
    data(dataglm) -xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit"))
    #> Warning message: provide lo.theta and hi.theta
    lik <- curve_lik(xx, dataglm) -tibble::tibble(lik[[1]])
    #> # A tibble: 300 x 1 -#> `lik[[1]]`$values $likelihood $loglikelihood $support $deviancestat -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -1.41 9.26e-21 -9.79 0.0000560 9.79 -#> 2 -1.39 1.05e-20 -9.66 0.0000638 9.66 -#> 3 -1.37 1.20e-20 -9.53 0.0000726 9.53 -#> 4 -1.35 1.37e-20 -9.40 0.0000826 9.40 -#> 5 -1.34 1.55e-20 -9.27 0.0000940 9.27 -#> 6 -1.32 1.76e-20 -9.15 0.000107 9.15 -#> 7 -1.30 2.00e-20 -9.02 0.000121 9.02 -#> 8 -1.28 2.27e-20 -8.89 0.000137 8.89 -#> 9 -1.26 2.57e-20 -8.77 0.000156 8.77 -#> 10 -1.24 2.92e-20 -8.64 0.000176 8.64 -#> # … with 290 more rows
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_mean.html b/docs/reference/curve_mean.html deleted file mode 100644 index 6b44c5c..0000000 --- a/docs/reference/curve_mean.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - - - - -Mean Interval Consonance Function — curve_mean • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen -parameter in a statistical test that compares means and places the interval -limits for each interval level into a data frame along with the corresponding -p-values and s-values.

    -
    - -
    curve_mean(
    -  x,
    -  y,
    -  data,
    -  paired = F,
    -  method = "default",
    -  replicates = 1000,
    -  steps = 10000,
    -  table = TRUE
    -)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    x

    Variable that contains the data for the first group being compared.

    y

    Variable that contains the data for the second group being compared.

    data

    Data frame from which the variables are being extracted from.

    paired

    Indicates whether the statistical test is a paired difference test. -By default, it is set to "F",which means the function will be an unpaired -statistical test comparing two independent groups.Inserting "paired" will -change the test to a paired difference test.

    method

    By default this is turned off (set to "default"), but -allows for bootstrapping if "boot" is insertedinto the function call.

    replicates

    Indicates how many bootstrap replicates are to be performed. -The defaultis currently 20000 but more may be desirable, especially to make -the functions more smooth.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    # \donttest{ -# Simulate random data -GroupA <- runif(100, min = 0, max = 100) -GroupB <- runif(100, min = 0, max = 100) -RandomData <- data.frame(GroupA, GroupB) -bob <- curve_mean(GroupA, GroupB, RandomData) -tibble::tibble(bob[[1]])
    #> # A tibble: 10,000 x 1 -#> `bob[[1]]`$lowe… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 4.18 4.18 0 0 0.5 1 -#> 2 4.18 4.18 0.00105 0.0001 0.500 1.000 -#> 3 4.18 4.18 0.00210 0.0002 0.500 1.000 -#> 4 4.18 4.18 0.00315 0.000300 0.500 1.000 -#> 5 4.18 4.18 0.00420 0.0004 0.500 1.000 -#> 6 4.18 4.18 0.00525 0.0005 0.500 1.000 -#> 7 4.18 4.18 0.00630 0.000600 0.500 0.999 -#> 8 4.18 4.18 0.00735 0.0007 0.500 0.999 -#> 9 4.18 4.18 0.00840 0.0008 0.500 0.999 -#> 10 4.18 4.19 0.00945 0.0009 0.500 0.999 -#> # … with 9,990 more rows, and 1 more variable: $svalue <dbl>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_meta.html b/docs/reference/curve_meta.html deleted file mode 100644 index 9d32e48..0000000 --- a/docs/reference/curve_meta.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - -Meta-analytic Consonance Function — curve_meta • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen -parameter in the meta-analysis done by the metafor package and places the -interval limits for each interval level into a data frame along with the -corresponding p-values and s-values.

    -
    - -
    curve_meta(x, measure = "default", steps = 10000, table = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    x

    Object where the meta-analysis parameters are stored, typically a -list produced by 'metafor'

    measure

    Indicates whether the object has a log transformation or is normal/default. -The default setting is "default. If the measure is set to "ratio", it will take -logarithmically transformed values and convert them back to normal values in the dataframe. -This is typically a setting used for binary outcomes such as risk ratios, -hazard ratios, and odds ratios.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    # \donttest{ -# Simulate random data for two groups in two studies -GroupAData <- runif(20, min = 0, max = 100) -GroupAMean <- round(mean(GroupAData), digits = 2) -GroupASD <- round(sd(GroupAData), digits = 2) - -GroupBData <- runif(20, min = 0, max = 100) -GroupBMean <- round(mean(GroupBData), digits = 2) -GroupBSD <- round(sd(GroupBData), digits = 2) - -GroupCData <- runif(20, min = 0, max = 100) -GroupCMean <- round(mean(GroupCData), digits = 2) -GroupCSD <- round(sd(GroupCData), digits = 2) - -GroupDData <- runif(20, min = 0, max = 100) -GroupDMean <- round(mean(GroupDData), digits = 2) -GroupDSD <- round(sd(GroupDData), digits = 2) - -# Combine the data - -StudyName <- c("Study1", "Study2") -MeanTreatment <- c(GroupAMean, GroupCMean) -MeanControl <- c(GroupBMean, GroupDMean) -SDTreatment <- c(GroupASD, GroupCSD) -SDControl <- c(GroupBSD, GroupDSD) -NTreatment <- c(20, 20) -NControl <- c(20, 20) - -metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl) - -# Use metafor to calculate the standardized mean difference - -library(metafor)
    #> Loading required package: Matrix
    #> Loading 'metafor' package (version 2.1-0). For an overview -#> and introduction to the package please type: help(metafor).
    # -dat <- escalc(measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf) -# -# Pool the data using a particular method. Here "FE" is the fixed-effects model -# -res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) -# -# Calculate the intervals using the metainterval function -# -metaf <- curve_meta(res) -# -tibble::tibble(metaf[[1]])
    #> # A tibble: 10,000 x 1 -#> `metaf[[1]]`$lo… $upper.limit $intrvl.level $pvalue $svalue $intrvl.width -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.159 0.159 0 1 0. 0 -#> 2 0.159 0.159 0.0001 1.000 1.44e-4 0.0000565 -#> 3 0.159 0.159 0.0002 1.000 2.89e-4 0.000113 -#> 4 0.159 0.159 0.000300 1.000 4.33e-4 0.000169 -#> 5 0.159 0.159 0.0004 1.000 5.77e-4 0.000226 -#> 6 0.159 0.159 0.0005 1.000 7.22e-4 0.000282 -#> 7 0.159 0.159 0.000600 0.999 8.66e-4 0.000339 -#> 8 0.159 0.159 0.0007 0.999 1.01e-3 0.000395 -#> 9 0.159 0.159 0.0008 0.999 1.15e-3 0.000452 -#> 10 0.159 0.159 0.0009 0.999 1.30e-3 0.000508 -#> # … with 9,990 more rows, and 1 more variable: $cdf <dbl>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_rev.html b/docs/reference/curve_rev.html deleted file mode 100644 index 7187c2f..0000000 --- a/docs/reference/curve_rev.html +++ /dev/null @@ -1,286 +0,0 @@ - - - - - - - - -Reverse Engineer Consonance / Likelihood Functions Using the Point -Estimate and Confidence Limits — curve_rev • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Using the confidence limits and point estimates from a dataset, one can use -these estimates to compute thousands of consonance intervals and graph the -intervals to form a consonance and surprisal function.

    -
    - -
    curve_rev(
    -  point,
    -  LL,
    -  UL,
    -  type = "c",
    -  measure = "default",
    -  steps = 10000,
    -  table = TRUE
    -)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    point

    The point estimate from an analysis. Ex: 1.20

    LL

    The lower confidence limit from an analysis Ex: 1.0

    UL

    The upper confidence limit from an analysis Ex: 1.4

    type

    Indicates whether the produced result should be a consonance -function or a likelihood function. The default is "c" for consonance and -likelihood can be set via "l".

    measure

    The type of data being used. If they involve mean differences,

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -

    Examples

    -
    # \donttest{ -# From a real published study. Point estimate of the result was hazard ratio of 1.61 and -# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. -# -df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") - -tibble::tibble(df[[1]])
    #> # A tibble: 9,999 x 1 -#> `df[[1]]`$lower… $upper.limit $intrvl.width $intrvl.level $cdf $pvalue -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 0.624 4.15 3.53 1.000 1.000 10.00e-5 -#> 2 0.651 3.98 3.33 1.000 1.000 2.00e-4 -#> 3 0.667 3.88 3.22 1.000 1.000 3.00e-4 -#> 4 0.680 3.81 3.13 1.000 1.000 4.00e-4 -#> 5 0.690 3.76 3.07 1.000 1.000 5.00e-4 -#> 6 0.698 3.71 3.02 0.999 1.000 6.00e-4 -#> 7 0.705 3.68 2.97 0.999 1.000 7.00e-4 -#> 8 0.712 3.64 2.93 0.999 1.000 8.00e-4 -#> 9 0.717 3.61 2.90 0.999 1.000 9.00e-4 -#> 10 0.722 3.59 2.87 0.999 1.000 1.00e-3 -#> # … with 9,989 more rows, and 1 more variable: $svalue <dbl>
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_surv.html b/docs/reference/curve_surv.html deleted file mode 100644 index 773352a..0000000 --- a/docs/reference/curve_surv.html +++ /dev/null @@ -1,242 +0,0 @@ - - - - - - - - -Produce Consonance Intervals for Survival Data — curve_surv • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Computes thousands of consonance (confidence) intervals for the chosen -parameter in the Cox model computed by the 'survival' package and places -the interval limits for each interval level into a data frame along -with the corresponding p-value and s-value.

    -
    - -
    curve_surv(data, x, steps = 10000, table = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    data

    Object where the Cox model is stored, typically a list produced by the -'survival' package.

    x

    Predictor of interest within the survival model for which the -consonance intervals should be computed.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - - -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/curve_table.html b/docs/reference/curve_table.html deleted file mode 100644 index c309094..0000000 --- a/docs/reference/curve_table.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - - -Produce Tables For concurve Functions — curve_table • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Produces publication-ready tables with relevant statistics of interest -for functions produced from the concurve package.

    -
    - -
    curve_table(data, levels, type = "c", format = "data.frame")
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    data

    Dataframe from a concurve function to produce a table for

    levels

    Levels of the consonance intervals or likelihood intervals that should be -included in the table.

    type

    Indicates whether the table is for a consonance function or likelihood function. -The default is set to "c" for consonance and can be switched to "l" for likelihood.

    format

    The format of the tables. The options include "data.frame" which is the -default, "tibble", "docx" (which creates a table for a word document), "pptx" (which -creates a table for powerpoint), "latex", (which creates a table for a TeX document), and -"image", which produces an image of the table.

    - - -

    Examples

    -
    # \donttest{ - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") - -(z <- curve_table(intervalsdf[[1]], format = "data.frame"))
    #> Lower Limit Upper Limit Interval Width Interval Level (%) CDF P-value -#> 2501 -0.039 0.002 0.041 25.0 0.625 0.750 -#> 5001 -0.062 0.025 0.087 50.0 0.750 0.500 -#> 7501 -0.093 0.055 0.148 75.0 0.875 0.250 -#> 8001 -0.101 0.064 0.165 80.0 0.900 0.200 -#> 8501 -0.111 0.074 0.185 85.0 0.925 0.150 -#> 9001 -0.125 0.087 0.212 90.0 0.950 0.100 -#> 9501 -0.145 0.107 0.252 95.0 0.975 0.050 -#> 9751 -0.163 0.126 0.289 97.5 0.988 0.025 -#> 9901 -0.185 0.147 0.332 99.0 0.995 0.010 -#> S-value (bits) -#> 2501 0.415 -#> 5001 1.000 -#> 7501 2.000 -#> 8001 2.322 -#> 8501 2.737 -#> 9001 3.322 -#> 9501 4.322 -#> 9751 5.322 -#> 9901 6.644
    (z <- curve_table(intervalsdf[[1]], format = "tibble"))
    #> # A tibble: 9 x 1 -#> subdf$`Lower Li… $`Upper Limit` $`Interval Widt… $`Interval Leve… $CDF -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.039 0.002 0.041 25 0.625 -#> 2 -0.062 0.025 0.087 50 0.75 -#> 3 -0.093 0.055 0.148 75 0.875 -#> 4 -0.101 0.064 0.165 80 0.9 -#> 5 -0.111 0.074 0.185 85 0.925 -#> 6 -0.125 0.087 0.212 90 0.95 -#> 7 -0.145 0.107 0.252 95 0.975 -#> 8 -0.163 0.126 0.289 97.5 0.988 -#> 9 -0.185 0.147 0.332 99 0.995 -#> # … with 2 more variables: $`P-value` <dbl>, $`S-value (bits)` <dbl>
    (z <- curve_table(intervalsdf[[1]], format = "latex"))
    #> -#> -#> | | Lower Limit| Upper Limit| Interval Width| Interval Level (%)| CDF| P-value| S-value (bits)| -#> |:----|-----------:|-----------:|--------------:|------------------:|-----:|-------:|--------------:| -#> |2501 | -0.039| 0.002| 0.041| 25.0| 0.625| 0.750| 0.415| -#> |5001 | -0.062| 0.025| 0.087| 50.0| 0.750| 0.500| 1.000| -#> |7501 | -0.093| 0.055| 0.148| 75.0| 0.875| 0.250| 2.000| -#> |8001 | -0.101| 0.064| 0.165| 80.0| 0.900| 0.200| 2.322| -#> |8501 | -0.111| 0.074| 0.185| 85.0| 0.925| 0.150| 2.737| -#> |9001 | -0.125| 0.087| 0.212| 90.0| 0.950| 0.100| 3.322| -#> |9501 | -0.145| 0.107| 0.252| 95.0| 0.975| 0.050| 4.322| -#> |9751 | -0.163| 0.126| 0.289| 97.5| 0.988| 0.025| 5.322| -#> |9901 | -0.185| 0.147| 0.332| 99.0| 0.995| 0.010| 6.644|
    # } - -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/defunct.html b/docs/reference/defunct.html deleted file mode 100644 index 072f44a..0000000 --- a/docs/reference/defunct.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - - -Deprecated functions in <span class="pkg">concurve</span>. — defunct • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -concurve. — defunct" /> - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    The functions listed below are deprecated. Please use the listed alternatives.

    -
    - -
    plotpint(...)
    -
    -plotsint(...)
    -
    -meanintervals(...)
    -
    -metaintervals(...)
    -
    -genintervals(...)
    -
    -corrintervals(...)
    -
    -survintervals(...)
    -
    -rev_eng(...)
    - - -

    plotpint

    - - - -

    For plotpint, use ggconcurve.

    -

    plotsint

    - - - -

    For plotsint, use ggconcurve.

    -

    meanintervals

    - - - -

    For meanintervals, use curve_mean.

    -

    metaintervals

    - - - -

    For metaintervals, use curve_meta.

    -

    genintervals

    - - - -

    For genintervals, use curve_gen.

    -

    corrintervals

    - - - -

    For corrintervals, use curve_corr.

    -

    survintervals

    - - - -

    For survintervals, use curve_surv.

    -

    rev_eng

    - - - -

    For rev_eng, use curve_rev.

    - -
    - -
    - - - -
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zl5gf05)`w4u-xx=zrfBnh`xIgci=+(wesEb0hW73wuvtS`(>?ewDUTnmxLvBTAfF9 zNUq~8kXm{QS4A;DP~=f2WYPDiaJ=)crp^f*cWb!8W|euRS*iQxDz55q2g%LeH-428 z#-_yJrKscU4R7B`MC6wW&8&OuqfYiVaNYrrK=-#h&T#FJm!8;!$7gJ15?z}(Vju9x zi)&ugYh$?o#&{P)N>JGr+xo*9gUu_{_RH?O}*2ZR#_@ zccydIA`)I*spF+1KDRG4-^vZpOIK2Fw)W77xRSN~dgP4O#&G?J53NOl6i_A_9vque zd1!M3=3*feOQAe+GJT?K_cwl|b;7XXtd##ndadVu8hJmfZC?p5j%V5b)qIs9*hd3N=RmCe1yUEUrs5?Sa~{ z&eN#QpJsHi%>a+qrN=J)`A4{}j{#bxz5I`lE=rV$iD|MXztsGP=gdN^e(!+uAvY_t zvPw8`P+jqY$f^FW_j>23Mb^5-!P{G)UiG)L%v~78BwkIC*7&vWCm?ZxqU0{!E$T6) zpJFG{(s$1T%ZKzQ>2uv%NjdVV-j{)8HSeM`6kT;Z36OOC&8)qh?hp#>IkH-{YO|6&Qx zBL}AMjZ83u_^%(1%pqB0;c+~fWc~7A{}y>#;1`TEhZX#}nDUwtx?k)K - - - - - - - -Plots the P-Value (Consonance), S-value (Surprisal), -and Likelihood Function via ggplot2 — ggconcurve • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

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    - -
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    - - -
    -

    Takes the dataframe produced by the interval functions and -plots the p-values/s-values, consonance (confidence) levels, and -the interval estimates to produce a p-value/s-value function -using ggplot2 graphics.

    -
    - -
    ggconcurve(
    -  data,
    -  type = "c",
    -  measure = "default",
    -  levels = 0.95,
    -  nullvalue = FALSE,
    -  position = "pyramid",
    -  title = "Interval Function",
    -  subtitle = "The function displays intervals at every level.",
    -  xaxis = expression(Theta ~ "Range of Values"),
    -  yaxis = "P-value",
    -  color = "#000000",
    -  fill = "#239a98"
    -)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data

    The dataframe produced by one of the interval functions -in which the intervals are stored.

    type

    Choose whether to plot a "consonance" function, a -"surprisal" function or "likelihood". The default option is set to "c". -The type must be set in quotes, for example ggconcurve (type = "s") or -ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, -"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood -and "d" for deviance function.

    measure

    Indicates whether the object has a log transformation -or is normal/default. The default setting is "default". If the measure -is set to "ratio", it will take logarithmically transformed values and -convert them back to normal values in the dataframe. This is typically a -setting used for binary outcomes and their measures such as risk ratios, -hazard ratios, and odds ratios.

    levels

    Indicates which interval levels should be plotted on the function. -By default it is set to 0.95 to plot the 95 -but more levels can be plotted by using the c() function for example, -levels = c(0.50, 0.75, 0.95).

    nullvalue

    Indicates whether the null value for the measure -should be plotted. By default, it is set to FALSE, meaning it will not be -plotted as a vertical line. Changing this to TRUE, will plot a vertical -line at 0 when the measure is set to " default" and a vertical line at -1 when the measure is set to "ratio". For example, -ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). -This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will -stand right side up, like a pyramid. However, it can also be inverted -via the option "inverted". This will also change the sequence of the -y-axes to match the orientation.This can be set as such, -ggconcurve(type = "c", data = df, position = "inverted").

    title

    A custom title for the graph. By default, it is -set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "c", -data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set -to "The function contains consonance/confidence intervals at every level -and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, -it is set to "Range of Values. -In order to set a x-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, -it is set to "Consonance Level". -In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points -and the ribbons in the graph. By default, it is set to color = "#555555". -The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, color = "#333333").

    fill

    Item that allows the user to choose the color of the ribbons in the graph. -By default, it is set to fill = "#239a98". The inputs must be in quotes. For example, -ggconcurve(type = "c", data = x, fill = "#333333").

    - -

    Value

    - -

    Plot with intervals at every consonance level graphed with their corresponding -p-values and compatibility levels.

    - -

    Examples

    -
    # \donttest{ -# Simulate random data - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") -(function1 <- ggconcurve(type = "c", intervalsdf[[1]]))
    # } -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index 0085a13..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,289 +0,0 @@ - - - - - - - - -Function reference • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
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    curve_boot()

    -

    Generate Consonance Functions via Bootstrapping

    -

    curve_compare()

    -

    Compares two functions and produces an AUC score to show the amount of consonance.

    -

    curve_corr()

    -

    Computes Consonance Intervals for Correlations

    -

    curve_gen()

    -

    General Consonance Functions Using Profile Likelihood, Wald, -or the bootstrap method for linear models.

    -

    curve_lik()

    -

    Compute the Profile Likelihood Functions

    -

    curve_mean()

    -

    Mean Interval Consonance Function

    -

    curve_meta()

    -

    Meta-analytic Consonance Function

    -

    curve_rev()

    -

    Reverse Engineer Consonance / Likelihood Functions Using the Point -Estimate and Confidence Limits

    -

    curve_surv()

    -

    Produce Consonance Intervals for Survival Data

    -

    curve_table()

    -

    Produce Tables For concurve Functions

    -

    ggconcurve()

    -

    Plots the P-Value (Consonance), S-value (Surprisal), -and Likelihood Function via ggplot2

    -

    plot_compare()

    -

    Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2

    -
    - - -
    - - - -
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z*|2^aAb36v0m#Y(7xm=d;{t!67lN;CAxHneOtvOm49T2c7@-2BB8N|VeK~4F6i%!B z_h1EISgt$uKR#a^NzV1>ElmD?%`J-r<|w#*eb3oVw5kU40OWv-GT)bN2nTpE5^7t* zb(DRxRjJ);s5!{{y=5F?jv6jF5;psh1`Fo z>Yi{r04`tD*rC+I@2u+o_+d!0d=Yl0`HZyn-<$aNrNW<@{&U6rAODB{|KtC^{P5k5 aPrrN1p4z5lb?pTFBPR4nFz11$`~L&u5+*VL diff --git a/docs/reference/plot_compare.html b/docs/reference/plot_compare.html deleted file mode 100644 index 73363c4..0000000 --- a/docs/reference/plot_compare.html +++ /dev/null @@ -1,343 +0,0 @@ - - - - - - - - -Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2 — plot_compare • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    -

    Compares the p-value/s-value, and likelihood functions using ggplot2 graphics.

    -
    - -
    plot_compare(
    -  data1,
    -  data2,
    -  type = "c",
    -  measure = "default",
    -  nullvalue = FALSE,
    -  position = "pyramid",
    -  title = "Interval Functions",
    -  subtitle = "The function displays intervals at every level.",
    -  xaxis = expression(Theta ~ "Range of Values"),
    -  yaxis = "P-value",
    -  color = "#000000",
    -  fill1 = "#239a98",
    -  fill2 = "#d46c5b"
    -)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data1

    The first dataframe produced by one of the interval functions in which the -intervals are stored.

    data2

    The second dataframe produced by one of the interval functions in which the -intervals are stored.

    type

    Choose whether to plot a "consonance" function, a -"surprisal" function or "likelihood". The default option is set to "c". -The type must be set in quotes, for example ggconcurve (type = "s") or -ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, -"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood -and "d" for deviance function.

    measure

    Indicates whether the object has a log transformation -or is normal/default. The default setting is "default". If the measure -is set to "ratio", it will take logarithmically transformed values and -convert them back to normal values in the dataframe. This is typically a -setting used for binary outcomes and their measures such as risk ratios, -hazard ratios, and odds ratios.

    nullvalue

    Indicates whether the null value for the measure -should be plotted. By default, it is set to FALSE, meaning it will not be -plotted as a vertical line. Changing this to TRUE, will plot a vertical -line at 0 when the measure is set to " default" and a vertical line at -1 when the measure is set to "ratio". For example, -ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). -This feature is not yet available for surprisal functions.

    position

    Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will -stand right side up, like a pyramid. However, it can also be inverted -via the option "inverted". This will also change the sequence of the -y-axes to match the orientation.This can be set as such, -ggconcurve(type = "c", data = df, position = "inverted").

    title

    A custom title for the graph. By default, it is -set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "c", -data = x, title = "Custom Title").

    subtitle

    A custom subtitle for the graph. By default, it is set -to "The function contains consonance/confidence intervals at every level -and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").

    xaxis

    A custom x-axis title for the graph. By default, -it is set to "Range of Values. -In order to set a x-axis title, it must be in quotes. For example, -ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").

    yaxis

    A custom y-axis title for the graph. By default, -it is set to "Consonance Level". -In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, yxis = "Confidence Level").

    color

    Item that allows the user to choose the color of the points -and the ribbons in the graph. By default, it is set to color = "#555555". -The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, color = "#333333").

    fill1

    Item that allows the user to choose the color of the ribbons in the graph -for data1. By default, it is set to fill1 = "#239a98". The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, fill1 = "#333333").

    fill2

    Item that allows the user to choose the color of the ribbons in the graph -for data1. By default, it is set to fill2 = "#d46c5b". The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, fill2 = "#333333").

    - -

    Value

    - -

    A plot that compares two functions.

    - -

    Examples

    -
    # \donttest{ -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) -RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) -RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) - -(plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s"))
    # } - -
    -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/reference/tidyeval.html b/docs/reference/tidyeval.html deleted file mode 100644 index 80170e1..0000000 --- a/docs/reference/tidyeval.html +++ /dev/null @@ -1,269 +0,0 @@ - - - - - - - - -Tidy eval helpers — tidyeval • concurve - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    - - -
    - -
      -
    • sym() creates a symbol from a string and -syms() creates a list of symbols from a -character vector.

    • -
    • enquo() and -enquos() delay the execution of one or -several function arguments. enquo() returns a single quoted -expression, which is like a blueprint for the delayed computation. -enquos() returns a list of such quoted expressions.

    • -
    • expr() quotes a new expression locally. It -is mostly useful to build new expressions around arguments -captured with enquo() or enquos(): -expr(mean(!!enquo(arg), na.rm = TRUE)).

    • -
    • as_name() transforms a quoted variable name -into a string. Supplying something else than a quoted variable -name is an error.

      -

      That's unlike as_label() which also returns -a single string but supports any kind of R object as input, -including quoted function calls and vectors. Its purpose is to -summarise that object into a single label. That label is often -suitable as a default name.

      -

      If you don't know what a quoted expression contains (for instance -expressions captured with enquo() could be a variable -name, a call to a function, or an unquoted constant), then use -as_label(). If you know you have quoted a simple variable -name, or would like to enforce this, use as_name().

    • -
    - -

    To learn more about tidy eval and how to use these tools, visit -https://tidyeval.tidyverse.org and the -Metaprogramming -section of Advanced R.

    -
    - - - - -
    - -
    - - - -
    - - - - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index 56be766..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,57 +0,0 @@ - - - - https://data.lesslikely.com/concurve//index.html - - - https://data.lesslikely.com/concurve//reference/concurve-package.html - - - https://data.lesslikely.com/concurve//reference/curve_boot.html - - - https://data.lesslikely.com/concurve//reference/curve_compare.html - - - https://data.lesslikely.com/concurve//reference/curve_corr.html - - - https://data.lesslikely.com/concurve//reference/curve_gen.html - - - https://data.lesslikely.com/concurve//reference/curve_lik.html - - - https://data.lesslikely.com/concurve//reference/curve_mean.html - - - https://data.lesslikely.com/concurve//reference/curve_meta.html - - - https://data.lesslikely.com/concurve//reference/curve_rev.html - - - https://data.lesslikely.com/concurve//reference/curve_surv.html - - - https://data.lesslikely.com/concurve//reference/curve_table.html - - - https://data.lesslikely.com/concurve//reference/defunct.html - - - https://data.lesslikely.com/concurve//reference/ggconcurve.html - - - https://data.lesslikely.com/concurve//reference/plot_compare.html - - - https://data.lesslikely.com/concurve//reference/tidyeval.html - - - https://data.lesslikely.com/concurve//articles/examples.html - - - https://data.lesslikely.com/concurve//articles/stata.html - - diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd deleted file mode 100644 index 5b0c73e..0000000 --- a/man/curve_boot.Rd +++ /dev/null @@ -1,67 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_boot.R -\name{curve_boot} -\alias{curve_boot} -\title{Generate Consonance Functions via Bootstrapping} -\usage{ -curve_boot( - data = data, - func = func, - method = "bca", - replicates = 2000, - steps = 1000, - table = TRUE -) -} -\arguments{ -\item{data}{Dataset that is being used to create a consonance function.} - -\item{func}{Custom function that is used to create parameters of interest that -will be bootstrapped.} - -\item{method}{The boostrap method that will be used to generate the functions. -Methods include "bca" which is the default and "t".} - -\item{replicates}{Indicates how many bootstrap replicates are to be performed. -The defaultis currently 20000 but more may be desirable, especially to make -the functions more smooth.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\value{ -A list with the dataframe of values in the first list and the table -in the second if table = TRUE. -} -\description{ -Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages -to generate consonance distrbutions. -} -\examples{ - -\donttest{ -data(diabetes, package = "bcaboot") -Xy <- cbind(diabetes$x, diabetes$y) -rfun <- function(Xy) { - y <- Xy[, 11] - X <- Xy[, 1:10] - return(summary(lm(y ~ X))$adj.r.squared) -} - -x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000) - -ggconcurve(data = x[[1]]) -ggconcurve(data = x[[3]]) - -plot_compare(x[[1]], x[[3]]) -} - -} diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd deleted file mode 100644 index 0270d85..0000000 --- a/man/curve_compare.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_compare.R -\name{curve_compare} -\alias{curve_compare} -\title{Compares two functions and produces an AUC score to show the amount of consonance.} -\usage{ -curve_compare(data1, data2, type = "c", plot = TRUE, ...) -} -\arguments{ -\item{data1}{The first dataframe produced by one of the interval functions -in which the intervals are stored.} - -\item{data2}{The second dataframe produced by one of the interval functions in -which the intervals are stored.} - -\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or -"likelihood". The default option is set to "c". The type must be set in quotes, -for example ggconcurve (type = "s") or ggconcurve(type = "c"). Other options -include "pd" for the consonance distribution function, and "cd" for the consonance -density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" -for likelihood and "d" for deviance function.} - -\item{plot}{by default it is set to TRUE and will use the plot_compare() function -to plot the two functions.} - -\item{...}{Can be used to pass further arguments to plot_compare().} -} -\description{ -Compares the p-value/s-value, and likelihood functions and computes an AUC number. -} -\examples{ -\donttest{ -library(concurve) -GroupA <- rnorm(500) -GroupB <- rnorm(500) -RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) -RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2", method = "glm") -(curve_compare(intervalsdf[[1]], randomframe[[1]])) -(curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) -} - -} diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd deleted file mode 100644 index bbe100f..0000000 --- a/man/curve_corr.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_corr.R -\name{curve_corr} -\alias{curve_corr} -\title{Computes Consonance Intervals for Correlations} -\usage{ -curve_corr(x, y, alternative, method, steps = 10000, table = TRUE) -} -\arguments{ -\item{x}{A vector that contains the data for one of the variables that will -be analyzed for correlational analysis.} - -\item{y}{A vector that contains the data for one of the variables that will -be analyzed for correlational analysis.} - -\item{alternative}{Indicates the alternative hypothesis and must be one of "two.sided", -"greater" or "less". You can specify just the initial letter. "greater" corresponds to -positive association, "less" to negative association.} - -\item{method}{A character string indicating which correlation coefficient is -to be used for the test. One of "pearson", "kendall", or "spearman", -can be abbreviated.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Computes consonance intervals to produce P- and S-value functions for -correlational analysesusing the cor.test function in base R and places the -interval limits for each interval levelinto a data frame along with the -corresponding p-values and s-values. -} -\examples{ - -GroupA <- rnorm(50) -GroupB <- rnorm(50) -joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") -tibble::tibble(joe[[1]]) -} diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd deleted file mode 100644 index 4e3b6a5..0000000 --- a/man/curve_gen.Rd +++ /dev/null @@ -1,57 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_gen.R -\name{curve_gen} -\alias{curve_gen} -\title{General Consonance Functions Using Profile Likelihood, Wald, -or the bootstrap method for linear models.} -\usage{ -curve_gen(model, var, method = "wald", steps = 1000, table = TRUE) -} -\arguments{ -\item{model}{The statistical model of interest -(ANOVA, regression, logistic regression) is to be indicated here.} - -\item{var}{The variable of interest from the model (coefficients, intercept) -for which the intervals are to be produced.} - -\item{method}{Chooses the method to be used to calculate the -consonance intervals. There are currently four methods: -"default", "wald", "lm", and "boot". The "default" method uses the profile -likelihood method to compute intervals and can be used for models created by -the 'lm' function. The "wald" method is typicallywhat most people are -familiar with when computing intervals based on the calculated standard error. -The "lm" method allows this function to be used for specific scenarios like -logistic regression and the 'glm' function. The "boot" method allows for -bootstrapping at certain levels.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Computes thousands of consonance (confidence) intervals for -the chosen parameter in the selected model -(ANOVA, ANCOVA, regression, logistic regression) and places -the interval limits for each interval level into a data frame along -with the corresponding p-values and s-values. -} -\examples{ - -\donttest{ -# Simulate random data -GroupA <- rnorm(50) -GroupB <- rnorm(50) -RandomData <- data.frame(GroupA, GroupB) -rob <- glm(GroupA ~ GroupB, data = RandomData) -bob <- curve_gen(rob, "GroupB", method = "glm") -tibble::tibble(bob[[1]]) -} - -} diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd deleted file mode 100644 index 2c37e48..0000000 --- a/man/curve_lik.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_lik.R -\name{curve_lik} -\alias{curve_lik} -\title{Compute the Profile Likelihood Functions} -\usage{ -curve_lik(likobject, data, table = TRUE) -} -\arguments{ -\item{likobject}{An object from the ProfileLikelihood package} - -\item{data}{The dataframe that was used to create the likelihood -object in the ProfileLikelihood package.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Compute the Profile Likelihood Functions -} -\examples{ - -library(ProfileLikelihood) -data(dataglm) -xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit")) -lik <- curve_lik(xx, dataglm) -tibble::tibble(lik[[1]]) -} diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd deleted file mode 100644 index a51a5da..0000000 --- a/man/curve_mean.Rd +++ /dev/null @@ -1,62 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_mean.R -\name{curve_mean} -\alias{curve_mean} -\title{Mean Interval Consonance Function} -\usage{ -curve_mean( - x, - y, - data, - paired = F, - method = "default", - replicates = 1000, - steps = 10000, - table = TRUE -) -} -\arguments{ -\item{x}{Variable that contains the data for the first group being compared.} - -\item{y}{Variable that contains the data for the second group being compared.} - -\item{data}{Data frame from which the variables are being extracted from.} - -\item{paired}{Indicates whether the statistical test is a paired difference test. -By default, it is set to "F",which means the function will be an unpaired -statistical test comparing two independent groups.Inserting "paired" will -change the test to a paired difference test.} - -\item{method}{By default this is turned off (set to "default"), but -allows for bootstrapping if "boot" is insertedinto the function call.} - -\item{replicates}{Indicates how many bootstrap replicates are to be performed. -The defaultis currently 20000 but more may be desirable, especially to make -the functions more smooth.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Computes thousands of consonance (confidence) intervals for the chosen -parameter in a statistical test that compares means and places the interval -limits for each interval level into a data frame along with the corresponding -p-values and s-values. -} -\examples{ - -# Simulate random data -GroupA <- runif(100, min = 0, max = 100) -GroupB <- runif(100, min = 0, max = 100) -RandomData <- data.frame(GroupA, GroupB) -bob <- curve_mean(GroupA, GroupB, RandomData) -tibble::tibble(bob[[1]]) -} diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd deleted file mode 100644 index b6e81a5..0000000 --- a/man/curve_meta.Rd +++ /dev/null @@ -1,82 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_meta.R -\name{curve_meta} -\alias{curve_meta} -\title{Meta-analytic Consonance Function} -\usage{ -curve_meta(x, measure = "default", steps = 10000, table = TRUE) -} -\arguments{ -\item{x}{Object where the meta-analysis parameters are stored, typically a -list produced by 'metafor'} - -\item{measure}{Indicates whether the object has a log transformation or is normal/default. -The default setting is "default. If the measure is set to "ratio", it will take -logarithmically transformed values and convert them back to normal values in the dataframe. -This is typically a setting used for binary outcomes such as risk ratios, -hazard ratios, and odds ratios.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Computes thousands of consonance (confidence) intervals for the chosen -parameter in the meta-analysis done by the metafor package and places the -interval limits for each interval level into a data frame along with the -corresponding p-values and s-values. -} -\examples{ - -# Simulate random data for two groups in two studies -GroupAData <- runif(20, min = 0, max = 100) -GroupAMean <- round(mean(GroupAData), digits = 2) -GroupASD <- round(sd(GroupAData), digits = 2) - -GroupBData <- runif(20, min = 0, max = 100) -GroupBMean <- round(mean(GroupBData), digits = 2) -GroupBSD <- round(sd(GroupBData), digits = 2) - -GroupCData <- runif(20, min = 0, max = 100) -GroupCMean <- round(mean(GroupCData), digits = 2) -GroupCSD <- round(sd(GroupCData), digits = 2) - -GroupDData <- runif(20, min = 0, max = 100) -GroupDMean <- round(mean(GroupDData), digits = 2) -GroupDSD <- round(sd(GroupDData), digits = 2) - -# Combine the data - -StudyName <- c("Study1", "Study2") -MeanTreatment <- c(GroupAMean, GroupCMean) -MeanControl <- c(GroupBMean, GroupDMean) -SDTreatment <- c(GroupASD, GroupCSD) -SDControl <- c(GroupBSD, GroupDSD) -NTreatment <- c(20, 20) -NControl <- c(20, 20) - -metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl) - -# Use metafor to calculate the standardized mean difference - -library(metafor) -# -dat <- escalc(measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf) -# -# Pool the data using a particular method. Here "FE" is the fixed-effects model -# -res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2) -# -# Calculate the intervals using the metainterval function -# -metaf <- curve_meta(res) -# -tibble::tibble(metaf[[1]]) -} diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd deleted file mode 100644 index 6ba3cc0..0000000 --- a/man/curve_rev.Rd +++ /dev/null @@ -1,55 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_rev.R -\name{curve_rev} -\alias{curve_rev} -\title{Reverse Engineer Consonance / Likelihood Functions Using the Point -Estimate and Confidence Limits} -\usage{ -curve_rev( - point, - LL, - UL, - type = "c", - measure = "default", - steps = 10000, - table = TRUE -) -} -\arguments{ -\item{point}{The point estimate from an analysis. Ex: 1.20} - -\item{LL}{The lower confidence limit from an analysis Ex: 1.0} - -\item{UL}{The upper confidence limit from an analysis Ex: 1.4} - -\item{type}{Indicates whether the produced result should be a consonance -function or a likelihood function. The default is "c" for consonance and -likelihood can be set via "l".} - -\item{measure}{The type of data being used. If they involve mean differences,} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Using the confidence limits and point estimates from a dataset, one can use -these estimates to compute thousands of consonance intervals and graph the -intervals to form a consonance and surprisal function. -} -\examples{ - -# From a real published study. Point estimate of the result was hazard ratio of 1.61 and -# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. -# -df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") - -tibble::tibble(df[[1]]) -} diff --git a/man/curve_surv.Rd b/man/curve_surv.Rd deleted file mode 100644 index 2f91d5b..0000000 --- a/man/curve_surv.Rd +++ /dev/null @@ -1,32 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_surv.R -\name{curve_surv} -\alias{curve_surv} -\title{Produce Consonance Intervals for Survival Data} -\usage{ -curve_surv(data, x, steps = 10000, table = TRUE) -} -\arguments{ -\item{data}{Object where the Cox model is stored, typically a list produced by the -'survival' package.} - -\item{x}{Predictor of interest within the survival model for which the -consonance intervals should be computed.} - -\item{steps}{Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.} - -\item{table}{Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.} -} -\description{ -Computes thousands of consonance (confidence) intervals for the chosen -parameter in the Cox model computed by the 'survival' package and places -the interval limits for each interval level into a data frame along -with the corresponding p-value and s-value. -} diff --git a/man/curve_table.Rd b/man/curve_table.Rd deleted file mode 100644 index 0d0014a..0000000 --- a/man/curve_table.Rd +++ /dev/null @@ -1,41 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/curve_table.R -\name{curve_table} -\alias{curve_table} -\title{Produce Tables For concurve Functions} -\usage{ -curve_table(data, levels, type = "c", format = "data.frame") -} -\arguments{ -\item{data}{Dataframe from a concurve function to produce a table for} - -\item{levels}{Levels of the consonance intervals or likelihood intervals that should be -included in the table.} - -\item{type}{Indicates whether the table is for a consonance function or likelihood function. -The default is set to "c" for consonance and can be switched to "l" for likelihood.} - -\item{format}{The format of the tables. The options include "data.frame" which is the -default, "tibble", "docx" (which creates a table for a word document), "pptx" (which -creates a table for powerpoint), "latex", (which creates a table for a TeX document), and -"image", which produces an image of the table.} -} -\description{ -Produces publication-ready tables with relevant statistics of interest -for functions produced from the concurve package. -} -\examples{ - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") - -(z <- curve_table(intervalsdf[[1]], format = "data.frame")) -(z <- curve_table(intervalsdf[[1]], format = "tibble")) -(z <- curve_table(intervalsdf[[1]], format = "latex")) -} diff --git a/man/ggconcurve.Rd b/man/ggconcurve.Rd deleted file mode 100644 index b64095c..0000000 --- a/man/ggconcurve.Rd +++ /dev/null @@ -1,114 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/ggconcurve.R -\name{ggconcurve} -\alias{ggconcurve} -\title{Plots the P-Value (Consonance), S-value (Surprisal), -and Likelihood Function via ggplot2} -\usage{ -ggconcurve( - data, - type = "c", - measure = "default", - levels = 0.95, - nullvalue = FALSE, - position = "pyramid", - title = "Interval Function", - subtitle = "The function displays intervals at every level.", - xaxis = expression(Theta ~ "Range of Values"), - yaxis = "P-value", - color = "#000000", - fill = "#239a98" -) -} -\arguments{ -\item{data}{The dataframe produced by one of the interval functions -in which the intervals are stored.} - -\item{type}{Choose whether to plot a "consonance" function, a -"surprisal" function or "likelihood". The default option is set to "c". -The type must be set in quotes, for example ggconcurve (type = "s") or -ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, -"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood -and "d" for deviance function.} - -\item{measure}{Indicates whether the object has a log transformation -or is normal/default. The default setting is "default". If the measure -is set to "ratio", it will take logarithmically transformed values and -convert them back to normal values in the dataframe. This is typically a -setting used for binary outcomes and their measures such as risk ratios, -hazard ratios, and odds ratios.} - -\item{levels}{Indicates which interval levels should be plotted on the function. -By default it is set to 0.95 to plot the 95% interval on the consonance function, -but more levels can be plotted by using the c() function for example, -levels = c(0.50, 0.75, 0.95).} - -\item{nullvalue}{Indicates whether the null value for the measure -should be plotted. By default, it is set to FALSE, meaning it will not be -plotted as a vertical line. Changing this to TRUE, will plot a vertical -line at 0 when the measure is set to " default" and a vertical line at -1 when the measure is set to "ratio". For example, -ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). -This feature is not yet available for surprisal functions.} - -\item{position}{Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will -stand right side up, like a pyramid. However, it can also be inverted -via the option "inverted". This will also change the sequence of the -y-axes to match the orientation.This can be set as such, -ggconcurve(type = "c", data = df, position = "inverted").} - -\item{title}{A custom title for the graph. By default, it is -set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "c", -data = x, title = "Custom Title").} - -\item{subtitle}{A custom subtitle for the graph. By default, it is set -to "The function contains consonance/confidence intervals at every level -and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").} - -\item{xaxis}{A custom x-axis title for the graph. By default, -it is set to "Range of Values. -In order to set a x-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, xaxis = "Hazard Ratio").} - -\item{yaxis}{A custom y-axis title for the graph. By default, -it is set to "Consonance Level". -In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, yxis = "Confidence Level").} - -\item{color}{Item that allows the user to choose the color of the points -and the ribbons in the graph. By default, it is set to color = "#555555". -The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, color = "#333333").} - -\item{fill}{Item that allows the user to choose the color of the ribbons in the graph. -By default, it is set to fill = "#239a98". The inputs must be in quotes. For example, -ggconcurve(type = "c", data = x, fill = "#333333").} -} -\value{ -Plot with intervals at every consonance level graphed with their corresponding -p-values and compatibility levels. -} -\description{ -Takes the dataframe produced by the interval functions and -plots the p-values/s-values, consonance (confidence) levels, and -the interval estimates to produce a p-value/s-value function -using ggplot2 graphics. -} -\examples{ - -# Simulate random data - -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) - -RandomData <- data.frame(GroupA, GroupB) - -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") -(function1 <- ggconcurve(type = "c", intervalsdf[[1]])) -} diff --git a/man/plot_compare.Rd b/man/plot_compare.Rd deleted file mode 100644 index c058ad5..0000000 --- a/man/plot_compare.Rd +++ /dev/null @@ -1,117 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/plot_compare.R -\name{plot_compare} -\alias{plot_compare} -\title{Compares the P-Value (Consonance), S-value (Surprisal), and Likelihood Function via ggplot2} -\usage{ -plot_compare( - data1, - data2, - type = "c", - measure = "default", - nullvalue = FALSE, - position = "pyramid", - title = "Interval Functions", - subtitle = "The function displays intervals at every level.", - xaxis = expression(Theta ~ "Range of Values"), - yaxis = "P-value", - color = "#000000", - fill1 = "#239a98", - fill2 = "#d46c5b" -) -} -\arguments{ -\item{data1}{The first dataframe produced by one of the interval functions in which the -intervals are stored.} - -\item{data2}{The second dataframe produced by one of the interval functions in which the -intervals are stored.} - -\item{type}{Choose whether to plot a "consonance" function, a -"surprisal" function or "likelihood". The default option is set to "c". -The type must be set in quotes, for example ggconcurve (type = "s") or -ggconcurve(type = "c"). Other options include "pd" for the consonance -distribution function, and "cd" for the consonance density function, -"l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood -and "d" for deviance function.} - -\item{measure}{Indicates whether the object has a log transformation -or is normal/default. The default setting is "default". If the measure -is set to "ratio", it will take logarithmically transformed values and -convert them back to normal values in the dataframe. This is typically a -setting used for binary outcomes and their measures such as risk ratios, -hazard ratios, and odds ratios.} - -\item{nullvalue}{Indicates whether the null value for the measure -should be plotted. By default, it is set to FALSE, meaning it will not be -plotted as a vertical line. Changing this to TRUE, will plot a vertical -line at 0 when the measure is set to " default" and a vertical line at -1 when the measure is set to "ratio". For example, -ggconcurve(type = "c", data = df, measure = "ratio", nullvalue = "present"). -This feature is not yet available for surprisal functions.} - -\item{position}{Determines the orientation of the P-value (consonance) function. -By default, it is set to "pyramid", meaning the p-value function will -stand right side up, like a pyramid. However, it can also be inverted -via the option "inverted". This will also change the sequence of the -y-axes to match the orientation.This can be set as such, -ggconcurve(type = "c", data = df, position = "inverted").} - -\item{title}{A custom title for the graph. By default, it is -set to "Consonance Function". In order to set a title, it must -be in quotes. For example, ggconcurve(type = "c", -data = x, title = "Custom Title").} - -\item{subtitle}{A custom subtitle for the graph. By default, it is set -to "The function contains consonance/confidence intervals at every level -and the P-values." In order to set a subtitle, it must be in quotes. -For example, ggconcurve(type = "c", data = x, subtitle = "Custom Subtitle").} - -\item{xaxis}{A custom x-axis title for the graph. By default, -it is set to "Range of Values. -In order to set a x-axis title, it must be in quotes. For example, -ggconcurve(type = "consonance", data = x, xaxis = "Hazard Ratio").} - -\item{yaxis}{A custom y-axis title for the graph. By default, -it is set to "Consonance Level". -In order to set a y-axis title, it must be in quotes. For example, -ggconcurve(type = "c", data = x, yxis = "Confidence Level").} - -\item{color}{Item that allows the user to choose the color of the points -and the ribbons in the graph. By default, it is set to color = "#555555". -The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, color = "#333333").} - -\item{fill1}{Item that allows the user to choose the color of the ribbons in the graph -for data1. By default, it is set to fill1 = "#239a98". The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, fill1 = "#333333").} - -\item{fill2}{Item that allows the user to choose the color of the ribbons in the graph -for data1. By default, it is set to fill2 = "#d46c5b". The inputs must be in quotes. -For example, ggconcurve(type = "c", data = x, fill2 = "#333333").} -} -\value{ -A plot that compares two functions. -} -\description{ -Compares the p-value/s-value, and likelihood functions using ggplot2 graphics. -} -\examples{ -\donttest{ -library(concurve) - -GroupA <- rnorm(500) -GroupB <- rnorm(500) -RandomData <- data.frame(GroupA, GroupB) -intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) -GroupA2 <- rnorm(500) -GroupB2 <- rnorm(500) -RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, family = gaussian, data = RandomData2) - -randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1000) - -(plot_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) -} - -} diff --git a/vignettes/examples.html b/vignettes/examples.html deleted file mode 100644 index a792cdb..0000000 --- a/vignettes/examples.html +++ /dev/null @@ -1,484 +0,0 @@ - - - - - - - - - - - - - - - -Examples in R - - - - - - - - - - - - - - - - - - - - - -

    Examples in R

    - - - -
    -

    Using Mean Differences

    -

    If we were to generate some random data from a normal distribution with the following code,

    - -

    and compare the means of these two “groups” using a t-test,

    - -

    we would likely see some differences, given that we have such a small sample in each group.

    - -

    We can see our P-value for the statistical test along with the computed 95% interval with the command above (which is given to us by default by the program). Thus, effect sizes that range from the lower bound of this interval to the upper bound are compatible with the test model at this consonance level.

    -

    However, as stated before, a 95% interval is only an artifact of the commonly used 5% alpha level for hypothesis testing and is nowhere near as informative as a function.

    -

    If we were to take the information from this data and calculate a P-value function where every single consonance interval and its corresponding P-value were plotted, we would be able to see the full range of effect sizes compatible with the test model at various levels.

    -

    It is relatively easy to produce such a function using the concurve package in R.

    -

    Install the package directly from CRAN

    - -

    or get a more slightly up-to-date version via GitHub.

    - -

    We’ll use the same data from above to calculate a P-value function and since we are focusing on mean differences using a t-test, we will use the curve_meta() function to calculate our consonance intervals and store them in a dataframe.

    - -

    Now thousands of consonance intervals at various levels have been stored in the dataframe “intervalsdf.” We can preview some of the entries via the tibble package.

    - -

    There’s a quick look at the first few entries of our dataframe.

    -

    We can plot this data using the ggconcurve() function.

    - -
    - -
    -

    Now we can see every consonance interval and its corresponding P-value and consonance level plotted. As stated before, a single 95% consonance interval is simply a slice through this function, which provides far more information as to what is compatible with the test model and its assumptions.

    -

    Furthermore, we can also produce a “surprisal” function by plotting every consonance interval and its corresponding S-value using the ggconcurve() function with type being set as “surprisal”.

    - -
    - -
    -

    The graph from the code above provides us with consonance levels and the maximum amount of information against the effect sizes contained in the consonance interval.

    -
    -
    -

    Simple Linear Models

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    We can also try this with other simple linear models.

    -

    Let’s simulate more normal data and fit a simple linear regression to it using ordinary least squares regression with the base R lm() function.

    - -

    We can see some of the basic statistics of our model including the 95% interval for our predictor (GroupB). Perhaps we want more information. Well we can do that! Using the curve_gen(), we can calculate several consonance intervals for the regression coefficient and then plot the consonance and surprisal functions.

    - -

    Now that we have our data frame, we can graph our function.

    - -
    - -
    - -
    - -
    -

    We can also compare these functions to likelihood functions (also called support intervals), and we’ll see that we get very similar results. We’ll do this using the ProfileLikelihood package.

    - -

    Now we plot our likelihood function and we can see what the maximum likelihood estimation is. Notice that it’s practically similar to the interval in the S-value function with 0 bits of information against it and and the consonance interval in the P-value function with a P-value of 1.

    - -
    - -
    -

    We’ve used a relatively easy example for this blog post, but the concurve package is also able to calculate consonance functions for multiple regressions, logistic regressions, ANOVAs, and meta-analyses (that have been produced by the metafor package).

    -
    -
    -

    Using Meta-Analysis Data

    -

    Here we present another quick example with a meta-analysis of simulated data.

    -

    First, we generate random data for two groups in two hypothetical studies

    - -

    We can then quickly combine the data in a dataframe.

    - -

    Then, we’ll use metafor to calculate the standardized mean difference.

    - -

    Next, we’ll pool the data using a fixed-effects common-effects model

    - -

    Let’s look at our output.

    - -
    Fixed-Effects Model (k = 2)
    -
    -Test for Heterogeneity: 
    -Q(df = 1) = 0.61, p-val = 0.44
    -
    -Model Results:
    -
    -estimate    se  zval  pval  ci.lb  ci.ub 
    -    0.16  0.22  0.73  0.47  -0.28   0.60  
    -
    ----
    -Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
    -

    Take a look at the pooled summary effect and its interval. Keep it in mind as we move onto constructing a consonance function.

    -

    We can now take the object produced by the meta-analysis and calculate a P-value and S-value function with it to see the full spectrum of effect sizes compatible with the test model at every level. We’ll use the curve_meta() function to do this.

    - -

    Now that we have our dataframe with every computed interval, we can plot the functions.

    - -
    - -
    -

    And our S-value function

    - -
    - -
    -

    Compare the span of these functions and the information they provide to the consonance interval provided by the forest plot. We are now no longer limited to interpreting an arbitrarily chosen interval by mindless analytic decisions often built into statistical packages by default.

    -
    - - - - - - - - - - - diff --git a/vignettes/stata.html b/vignettes/stata.html deleted file mode 100644 index e6985b2..0000000 --- a/vignettes/stata.html +++ /dev/null @@ -1,478 +0,0 @@ - - - - - - - - - - - - - - -Using Stata - - - - - - - - - - - - - - - - - - - - - -

    Using Stata

    - - - -

    Although concurve was originally designed to be used in R, it is possible to achieve very similar results in Stata. We can use some datasets that are built into Stata to show how to achieve this.

    -

    First, let’s load the auto2 dataset which contains data about cars and their characteristics.

    - -

    Browse the dataset in your databrowser to get more familiar with some of the variables. Let’s say we’re interested in the relationship between miles per gallon and price. We could fit a very simple linear model to assess that relationship.

    -

    First, let’s visualize the data with a scatterplot.

    - -
    -
    - -
    -


    That’s what our data looks like. Clearly there seems to be an inverse relationship between miles per gallon and price.

    -

    Now we could fit a very simple linear model with miles per gallon being the predictor and price being the outcome and get some estimates of the relationship.

    - -
    -
    - -
    -


    That’s what our output looks like.

    -

    Our output also gives us 95% consonance (confidence) intervals by default. The interval includes values as low as -344 and as high as -133. Here’s what our model looks graphed.

    -
    -
    - -
    -


    -

    Unfortunately, the program only gives us one interval at 95% and we’re interested in seeing every single interval at every level, and plotting it to form a function.

    -

    Here’s the code that we’ll be using to achieve that.

    - -

    That’s a lot and may seem intimidating at first, but I’ll explain it line by line.

    - -

    postfile” is the command that will be responsible for pasting the data from our overall loop into a new dataset. Here, we are telling Stata that the internal Stata memory used to hold these results (the post) will be named “topost” and that it will have five variables, “level”, “pvalue”, “svalue”, “lointerval”, and “upinterval.”

    -
      -
    • level” will contain the consonance level that corresponds to the limits of the interval, with “lointerval” being the lower bound of the interval and “upinterval” being the upper bound.

    • -
    • pvalue”is computed by taking 1 - “level”, which is alpha.

    • -
    • svalue”is computed by taking the \(-log_{2}\) of the computed P-value, and this column will be used to plot the surprisal function.

    • -
    • my_new_data” is the filename that we’ve assigned to our new dataset.

    • -
    • replace” indicates that if there is an existing filename that already exists, we’re willing to relace it.

    • -
    -

    Here are the next few major lines

    - -

    The command “forvalues” is responsible for taking a set of numbers that we provide it, and running the contents within the braces through those numbers. So here, we’ve set the local macro “i” to contain numbers between 10 and 99.99 for our consonance levels. Why 10? Stata cannot compute consonance intervals lower than 10%.

    -

    Our next line contains the actual contents of what we want to do. Here, it says that we will run a simple linear regression where mpg is the predictor and where price is the outcome, and that the outputs for each loop will be suppressed, hence the “quiet.”

    -

    Then, we have the command “level” with the local macro “i” inside of it. As you may already know, “level” dictates the consonance level that Stata provides us. By default, this is set to 95%, but here, we’ve set it “i”, which we established via “forvalues” as being set to numbers between 10 and 99.

    -

    The next line two lines

    - -

    indicate that we will take variables of a certain class r(), (this class contains the interval bounds we need) and place them within a matrix called E. Then we will list the contents of this matrix.

    - -

    From the contents of this matrix list, we will take the estimates from the fifth and sixth rows (look at the last two paranthesis of this line of code above and then the image below) in the first column which contain our consonance limits, with the fifth row containing the lower bound of the interval and the sixth containing the upper bound.

    -
    -
    - -
    -


    -

    We will place the contents from the fifth row into the second variable we set originally for our new dataset, which was “lointerval.” The contents of the sixth row will be placed into “upinterval.”

    -

    All potential values of “i” (10-99) will be placed into the first variable that we set, “level”. From this first variable, we can compute the second variable we set up, which was “Pvalue” and we’ve done that here by subtracting “level” from 1 and then dividing the whole equation by 100, so that our P-value can be on the proper scale. Our third variable, which is the longest, computes the “Svalue” by using the previous variable, the “Pvalue” and taking the \(-log_{2}\) of it.

    -

    The relationships between the variables on this line and the variables we set up in the very first line are dictated by the order of the commands we have set, and therefore they correspond to the same order.

    -

    “post topost” is writing the results from each loop as new observations in this data structure.

    -

    With that, our loop has concluded, and we can now tell Stata that “post” is no longer needed

    - -

    We then tell Stata to clear its memory to make room for the new dataset we just created and we can list the contents of this new dataset.

    - -

    Now we have an actual dataset with all the consonance intervals at all the levels we wanted, ranging from 10% all the way up to 99%.

    -

    In order to get a function, we’ll need to be able to graph these results, and that can be tricky since for each observation we have one y value (the consonance level), and two x values, the lower bound of the interval and the upper bound of the interval.

    -

    So a typical scatterplot will not work, since Stata will only accept one x value. To bypass this, we’ll have to use a paired-coordinate scatterplot which will allow us to plot two different y variables and two different x variables.

    -

    Of course, we don’t need two y variables, so we can set both options to the variable “level”, and then we can set our first x variable to “lointerval” and the second x variable to “upinterval.”

    -

    This can all be done with the following commands, which will also allow us to set the title and subtitle of the graph, along with the titles of the axes.

    - -

    However, I would recommend using the menu to customize the plots as much as possible. Simply go to the Graphics menu and select Twoway Graphs. Then create a new plot definition, and select the Advanced plots and choose a paired coordinate scatterplot and fill in the y variables, both of which will be “levels” and the x variables, which will be “lointerval” and “upinterval”.

    -
    -
    - -
    -


    -

    So now, here’s what our consonance function looks like.

    -
    -
    - -
    -


    And here’s what our surprisal function looks like.

    -
    -
    - -
    -


    It looks slighly better when the y-axis is transformed logarithmically…

    -
    -
    - -
    -


    …but then we lose our ability to differentiate between the effects contained in intervals with higher bits of information and S-values are already logarithmically scaled.

    -

    It’s clear that in both plots, we’re missing values of intervals with a consonance level of less than 10%, but unfortunately, this is the best Stata can do, and what we’ll have to work with. It may not look as pretty as an output from R, but it’s far more useful than blankly staring at a 95% interval and thinking that it is the only piece of information we have regarding compatibility of different effect estimates.

    -

    The code that I have pasted above can be used for most commands in Stata that have an option to calculate a consonance level. Thus, if there’s an option for “level”, then the commands above will work to produce a dataset of several consonance intervals.

    -

    For example, if we wished to fit a generalized linear model, here’s what our code would look like.

    - -

    We simply replace the first line within the loop with our intended command, just as I’ve replaced

    - -

    with

    - -

    If we wanted fit something more complex, like a multilevel mixed model that used restricted maximum likelihood, here’s what our code would look like

    - -

    Basically, our code doesn’t really change that much and with only a few lines of it, we are able to produce graphical tools that can better help us interpret the wide range of effect sizes that are compatible with the model and its assumptions.

    - - - - - - - - From 55b2b37273d2b8102b4390e543596e587500b421 Mon Sep 17 00:00:00 2001 From: Zad Chow Date: Wed, 4 Dec 2019 01:20:54 -0500 Subject: [PATCH 19/21] Updated website & vignettes --- .DS_Store | Bin 24580 -> 26628 bytes .Rbuildignore | 1 + DESCRIPTION | 12 +- LICENSE | 674 ++ LICENSE.md | 595 -- NEWS.md | 10 +- R/.DS_Store | Bin 6148 -> 6148 bytes R/curve_boot.R | 6 +- R/curve_compare.R | 14 +- R/curve_gen.R | 4 +- R/curve_meta.R | 30 +- R/defunct.R | 9 +- R/{ggconcurve.R => ggcurve.R} | 36 +- R/plot_compare.R | 34 +- README.Rmd | 34 +- README.md | 209 + _pkgdown.yml | 8 +- codemeta.json | 18 +- examples/Untitled.R | 2 +- examples/examples.R | 35 +- inst/WORDLIST | 12 +- inst/assets/bootstrap.css | 7331 ++++++++++++++++++++ inst/assets/pkgdown.css | 256 + inst/assets/pkgdown.js | 113 + inst/templates/navbar.html | 38 + man/.DS_Store | Bin 6148 -> 8196 bytes man/concurve-package.Rd | 4 +- man/curve_boot.Rd | 67 + man/curve_compare.Rd | 47 + man/curve_corr.Rd | 47 + man/curve_gen.Rd | 57 + man/curve_lik.Rd | 29 + man/curve_mean.Rd | 62 + man/curve_meta.Rd | 92 + man/curve_rev.Rd | 55 + man/curve_surv.Rd | 32 + man/curve_table.Rd | 41 + man/defunct.Rd | 22 +- man/figures/.DS_Store | Bin 6148 -> 6148 bytes man/ggcurve.Rd | 114 + man/plot_compare.Rd | 117 + pkgdown/extra.css | 6 + tests/spelling.R | 9 +- usethis.R | 7 + vignettes/american-medical-association.csl | 276 + vignettes/examples.Rmd | 414 +- vignettes/figures/densityfunction.png | Bin 0 -> 123882 bytes vignettes/references.bib | 296 + 48 files changed, 10323 insertions(+), 952 deletions(-) create mode 100644 LICENSE delete mode 100644 LICENSE.md rename R/{ggconcurve.R => ggcurve.R} (95%) create mode 100644 README.md create mode 100644 inst/assets/bootstrap.css create mode 100644 inst/assets/pkgdown.css create mode 100644 inst/assets/pkgdown.js create mode 100644 inst/templates/navbar.html create mode 100644 man/curve_boot.Rd create mode 100644 man/curve_compare.Rd create mode 100644 man/curve_corr.Rd create mode 100644 man/curve_gen.Rd create mode 100644 man/curve_lik.Rd create mode 100644 man/curve_mean.Rd create mode 100644 man/curve_meta.Rd create mode 100644 man/curve_rev.Rd create mode 100644 man/curve_surv.Rd create mode 100644 man/curve_table.Rd create mode 100644 man/ggcurve.Rd create mode 100644 man/plot_compare.Rd create mode 100755 vignettes/american-medical-association.csl create mode 100644 vignettes/figures/densityfunction.png create mode 100644 vignettes/references.bib diff --git a/.DS_Store b/.DS_Store index b18a67d3e8c13b02b1a0087fa57771f78aabb2df..55a2ed70d1cc353c02e3f5bc729265896e575c04 100644 GIT binary patch delta 2077 zcmeH|Yitx%7>3Wg-L^B++ia!NnQeEsEYdCQTDoneSC*DbLn)PNi!>m#TXw<4Wq0lN zf`BO!B#OZZUo_r`{!tJj0uvK8BnaXyCSn814;rHa#>8k8uNV!UnW;trfB4hHN#=6C z?@Z1)@AtgtYZlm?Z_0vsnxN8L?Xt>kx{59 zg=&uG)QXpl@NVEYzl1E^At-jeH2Y#2Wa{i*OsDEeKj$j~-P>3~fy^ff?h3uStJ6lO zGCfo&=ju-R5NPwl0^aqgEltyRHtYvQrUdjgP20f(^mX&w`bEWy!&}bU;H6-RqI*k< z^7T&NIvee+^-*cZ1uD9Aj-FZJ2f6AV!S_^V-=nPCVopjF#6G!j)28aWYJW*xfF|r7 z+wl|ho85yqXtaJFHqesv(;itD#WQklUXD_zXo5UV=p7!2-aQ;0y2p&t!3Jf_OcyI& zL0AzE4MpY+M))&q=B4S1Pn1JKPb#`GVnxa&zbL1~p1Y!PtB*2M3J7voTp9}vL}J!p zwbCp>?hu!aL`y&7H0XdnBKeZ(q16YR?hA@f^ zxDPw<5O(5W?7{Qci+wl(9Y=8-CrllgZL-R25o7d?D`)CSqvR~YHn0#|&yp<0zf-Z) zNOB;S7+}n{QtxTniCnEvRiwGne345m^FNy(&3O!u;|V;4r|}&2lkE54fPQ3lEhum1 zuTb^ImUdiA&X@CiCOLXv+c6tW_yYEv+&p=Ajp8mUR;4?PigYHz(KXRXSbkX$&luT~ zVpXO7i~^{%qU^?A)smjqKrquawUYXW^t=jIu)aZ3|5V;6gB6|~RMl0+Ad5q(p(V*k zoDcJkVC874tBqVU^3%_=i|li%ozYF>t}6ZVA_pePtb{(=`2y(OpvzpOq-S@frR8L> zK`z<|3-f1^jyc>JQkK@fsDrB9quB?t3tZmPimIC%=gg;X)9dVp=jhq|$}zV``-cTj z^Jv;%;hdbHuS}CnG&!P{GX9$+$wZSQYDEIol6I9ov+5-68hx6YYb5PD0=2Yi+Vuo7 zcM1<}xuKMNkC_o`pXz-#EGfi59XhEwtGMkn+I0JrysKnCRO{w@ z$PV%pEa%l~4mqbgeF8^Fww>W~*Q9bMH*!dj)6AxG(#3MRmE{gLY%bfatl?dblAK{> zgHns=NYgm@R(rZr6b0I~pg!v@_8uGOF8-LEr+pQ^nr19O8y2FSyR?TpbQyZFoUc1< zt~-iB?$lw7m`>fwSHB$(P-R;{`x`71|nziXAB9mZM2 vul_9lSYRAUx~k5%PqA62)SX6-?@USKO|jw!Dd?JEc=3PGt`D03-+=Zz_r(Em delta 3406 zcmeH}Yitx%6vywsTiTiFLT@Rwvu%gAOS^>@T1rdlg9@}jDJ?JCLU{dDGh6@GAjz~2*`+7*x!1J+ADo~&F&nJ33MUJTt0?@`0=amjKiV^WX)r?T%dhrii3`m>(SLAu??HB$eZWq#Q|u#lmVL^;WZ$r# z*j09o{lcyz9tu<>Ku0#b7>pv6qXLx}gYlSx1~g(CnlK*^U;)~&2whl;Wr*Totiuy{ z5*x4;&*KH`z)N@)Z{ckm!h1M@Gx!*vOS#0ZmQ3-+z+|!8Gl(=YCe=-@aWq;gR?V8z zt7aH2RFdGTJ2knf6_1y>D>5%nrjc!MnY4YIWB3dnL&Dg%LyZEJyQu7{HMvk7Vemdw zVLMu)j#7CnNnl6I)k=fMsad?i4p*yVRnDaNcD%;$sT_(brkCkrV{T^e`|Lw1;3w=0 z_7(e){Yu4C&<`n8Iu9~nz=t6iiUJJ7D2%2uR-y_MFbQ?2r&2bd1+&nKIcP@!K`h1+ ztB^fdft7d^k7F&Bb3LA+f(mTIvv?7^WC0nAu{vL53`>=TG;=c&ktFvD9eM&?Cdw^i z>TMHZIn8t;)_!RdO9LCkQE#)jF=SBhe)c+h*Xe}Q>>T@yea$Yh%hV57s3U%6*8phL z3vQ7-o#;? 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(2019) , and Greenland S, Chow ZR. (2019) . -License: GPL-3 +License: GPL-3 | file LICENSE URL: https://data.lesslikely.com/concurve/, - https://github.com/Zadchow/concurve, https://lesslikely.com/ -BugReports: https://github.com/Zadchow/concurve/issues + https://github.com/zadchow/concurve, https://lesslikely.com/ +BugReports: https://github.com/zadchow/concurve/issues Imports: bcaboot, boot, @@ -61,14 +61,16 @@ Suggests: roxygen2, spelling, testthat, - rmarkdown + rmarkdown, + Lock5Data VignetteBuilder: knitr ByteCompile: true Encoding: UTF-8 Language: en-US LazyData: true -RoxygenNote: 7.0.1 +Roxygen: list(markdown = TRUE) +RoxygenNote: 7.0.2 X-schema.org-keywords: confidence, compatibility, consonance, surprisal, interval, function, curve Depends: diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index 0010197..0000000 --- a/LICENSE.md +++ /dev/null @@ -1,595 +0,0 @@ -GNU General Public License -========================== - -_Version 3, 29 June 2007_ -_Copyright © 2007 Free Software Foundation, Inc. <>_ - -Everyone is permitted to copy and distribute verbatim copies of this license -document, but changing it is not allowed. - -## Preamble - -The GNU General Public License is a free, copyleft license for software and other -kinds of works. - -The licenses for most software and other practical works are designed to take away -your freedom to share and change the works. By contrast, the GNU General Public -License is intended to guarantee your freedom to share and change all versions of a -program--to make sure it remains free software for all its users. We, the Free -Software Foundation, use the GNU General Public License for most of our software; it -applies also to any other work released this way by its authors. You can apply it to -your programs, too. - -When we speak of free software, we are referring to freedom, not price. Our General -Public Licenses are designed to make sure that you have the freedom to distribute -copies of free software (and charge for them if you wish), that you receive source -code or can get it if you want it, that you can change the software or use pieces of -it in new free programs, and that you know you can do these things. - -To protect your rights, we need to prevent others from denying you these rights or -asking you to surrender the rights. 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Conveying Verbatim Copies - -You may convey verbatim copies of the Program's source code as you receive it, in any -medium, provided that you conspicuously and appropriately publish on each copy an -appropriate copyright notice; keep intact all notices stating that this License and -any non-permissive terms added in accord with section 7 apply to the code; keep -intact all notices of the absence of any warranty; and give all recipients a copy of -this License along with the Program. - -You may charge any price or no price for each copy that you convey, and you may offer -support or warranty protection for a fee. - -### 5. Conveying Modified Source Versions - -You may convey a work based on the Program, or the modifications to produce it from -the Program, in the form of source code under the terms of section 4, provided that -you also meet all of these conditions: - -* **a)** The work must carry prominent notices stating that you modified it, and giving a -relevant date. -* **b)** The work must carry prominent notices stating that it is released under this -License and any conditions added under section 7. This requirement modifies the -requirement in section 4 to “keep intact all notices”. -* **c)** You must license the entire work, as a whole, under this License to anyone who -comes into possession of a copy. This License will therefore apply, along with any -applicable section 7 additional terms, to the whole of the work, and all its parts, -regardless of how they are packaged. 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For a particular product received by a -particular user, “normally used” refers to a typical or common use of -that class of product, regardless of the status of the particular user or of the way -in which the particular user actually uses, or expects or is expected to use, the -product. A product is a consumer product regardless of whether the product has -substantial commercial, industrial or non-consumer uses, unless such uses represent -the only significant mode of use of the product. - -“Installation Information” for a User Product means any methods, -procedures, authorization keys, or other information required to install and execute -modified versions of a covered work in that User Product from a modified version of -its Corresponding Source. The information must suffice to ensure that the continued -functioning of the modified object code is in no case prevented or interfered with -solely because modification has been made. - -If you convey an object code work under this section in, or with, or specifically for -use in, a User Product, and the conveying occurs as part of a transaction in which -the right of possession and use of the User Product is transferred to the recipient -in perpetuity or for a fixed term (regardless of how the transaction is -characterized), the Corresponding Source conveyed under this section must be -accompanied by the Installation Information. But this requirement does not apply if -neither you nor any third party retains the ability to install modified object code -on the User Product (for example, the work has been installed in ROM). - -The requirement to provide Installation Information does not include a requirement to -continue to provide support service, warranty, or updates for a work that has been -modified or installed by the recipient, or for the User Product in which it has been -modified or installed. Access to a network may be denied when the modification itself -materially and adversely affects the operation of the network or violates the rules -and protocols for communication across the network. - -Corresponding Source conveyed, and Installation Information provided, in accord with -this section must be in a format that is publicly documented (and with an -implementation available to the public in source code form), and must require no -special password or key for unpacking, reading or copying. - -### 7. Additional Terms - -“Additional permissions” are terms that supplement the terms of this -License by making exceptions from one or more of its conditions. Additional -permissions that are applicable to the entire Program shall be treated as though they -were included in this License, to the extent that they are valid under applicable -law. If additional permissions apply only to part of the Program, that part may be -used separately under those permissions, but the entire Program remains governed by -this License without regard to the additional permissions. - -When you convey a copy of a covered work, you may at your option remove any -additional permissions from that copy, or from any part of it. (Additional -permissions may be written to require their own removal in certain cases when you -modify the work.) You may place additional permissions on material, added by you to a -covered work, for which you have or can give appropriate copyright permission. - -Notwithstanding any other provision of this License, for material you add to a -covered work, you may (if authorized by the copyright holders of that material) -supplement the terms of this License with terms: - -* **a)** Disclaiming warranty or limiting liability differently from the terms of -sections 15 and 16 of this License; or -* **b)** Requiring preservation of specified reasonable legal notices or author -attributions in that material or in the Appropriate Legal Notices displayed by works -containing it; or -* **c)** Prohibiting misrepresentation of the origin of that material, or requiring that -modified versions of such material be marked in reasonable ways as different from the -original version; or -* **d)** Limiting the use for publicity purposes of names of licensors or authors of the -material; or -* **e)** Declining to grant rights under trademark law for use of some trade names, -trademarks, or service marks; or -* **f)** Requiring indemnification of licensors and authors of that material by anyone -who conveys the material (or modified versions of it) with contractual assumptions of -liability to the recipient, for any liability that these contractual assumptions -directly impose on those licensors and authors. - -All other non-permissive additional terms are considered “further -restrictions” within the meaning of section 10. If the Program as you received -it, or any part of it, contains a notice stating that it is governed by this License -along with a term that is a further restriction, you may remove that term. If a -license document contains a further restriction but permits relicensing or conveying -under this License, you may add to a covered work material governed by the terms of -that license document, provided that the further restriction does not survive such -relicensing or conveying. - -If you add terms to a covered work in accord with this section, you must place, in -the relevant source files, a statement of the additional terms that apply to those -files, or a notice indicating where to find the applicable terms. - -Additional terms, permissive or non-permissive, may be stated in the form of a -separately written license, or stated as exceptions; the above requirements apply -either way. - -### 8. Termination - -You may not propagate or modify a covered work except as expressly provided under -this License. Any attempt otherwise to propagate or modify it is void, and will -automatically terminate your rights under this License (including any patent licenses -granted under the third paragraph of section 11). - -However, if you cease all violation of this License, then your license from a -particular copyright holder is reinstated **(a)** provisionally, unless and until the -copyright holder explicitly and finally terminates your license, and **(b)** permanently, -if the copyright holder fails to notify you of the violation by some reasonable means -prior to 60 days after the cessation. - -Moreover, your license from a particular copyright holder is reinstated permanently -if the copyright holder notifies you of the violation by some reasonable means, this -is the first time you have received notice of violation of this License (for any -work) from that copyright holder, and you cure the violation prior to 30 days after -your receipt of the notice. - -Termination of your rights under this section does not terminate the licenses of -parties who have received copies or rights from you under this License. If your -rights have been terminated and not permanently reinstated, you do not qualify to -receive new licenses for the same material under section 10. - -### 9. Acceptance Not Required for Having Copies - -You are not required to accept this License in order to receive or run a copy of the -Program. Ancillary propagation of a covered work occurring solely as a consequence of -using peer-to-peer transmission to receive a copy likewise does not require -acceptance. However, nothing other than this License grants you permission to -propagate or modify any covered work. These actions infringe copyright if you do not -accept this License. Therefore, by modifying or propagating a covered work, you -indicate your acceptance of this License to do so. - -### 10. Automatic Licensing of Downstream Recipients - -Each time you convey a covered work, the recipient automatically receives a license -from the original licensors, to run, modify and propagate that work, subject to this -License. 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For example, you may not impose a license fee, royalty, -or other charge for exercise of rights granted under this License, and you may not -initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging -that any patent claim is infringed by making, using, selling, offering for sale, or -importing the Program or any portion of it. - -### 11. Patents - -A “contributor” is a copyright holder who authorizes use under this -License of the Program or a work on which the Program is based. The work thus -licensed is called the contributor's “contributor version”. - -A contributor's “essential patent claims” are all patent claims owned or -controlled by the contributor, whether already acquired or hereafter acquired, that -would be infringed by some manner, permitted by this License, of making, using, or -selling its contributor version, but do not include claims that would be infringed -only as a consequence of further modification of the contributor version. For -purposes of this definition, “control” includes the right to grant patent -sublicenses in a manner consistent with the requirements of this License. - -Each contributor grants you a non-exclusive, worldwide, royalty-free patent license -under the contributor's essential patent claims, to make, use, sell, offer for sale, -import and otherwise run, modify and propagate the contents of its contributor -version. - -In the following three paragraphs, a “patent license” is any express -agreement or commitment, however denominated, not to enforce a patent (such as an -express permission to practice a patent or covenant not to sue for patent -infringement). To “grant” such a patent license to a party means to make -such an agreement or commitment not to enforce a patent against the party. - -If you convey a covered work, knowingly relying on a patent license, and the -Corresponding Source of the work is not available for anyone to copy, free of charge -and under the terms of this License, through a publicly available network server or -other readily accessible means, then you must either **(1)** cause the Corresponding -Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the -patent license for this particular work, or **(3)** arrange, in a manner consistent with -the requirements of this License, to extend the patent license to downstream -recipients. “Knowingly relying” means you have actual knowledge that, but -for the patent license, your conveying the covered work in a country, or your -recipient's use of the covered work in a country, would infringe one or more -identifiable patents in that country that you have reason to believe are valid. - -If, pursuant to or in connection with a single transaction or arrangement, you -convey, or propagate by procuring conveyance of, a covered work, and grant a patent -license to some of the parties receiving the covered work authorizing them to use, -propagate, modify or convey a specific copy of the covered work, then the patent -license you grant is automatically extended to all recipients of the covered work and -works based on it. - -A patent license is “discriminatory” if it does not include within the -scope of its coverage, prohibits the exercise of, or is conditioned on the -non-exercise of one or more of the rights that are specifically granted under this -License. You may not convey a covered work if you are a party to an arrangement with -a third party that is in the business of distributing software, under which you make -payment to the third party based on the extent of your activity of conveying the -work, and under which the third party grants, to any of the parties who would receive -the covered work from you, a discriminatory patent license **(a)** in connection with -copies of the covered work conveyed by you (or copies made from those copies), or **(b)** -primarily for and in connection with specific products or compilations that contain -the covered work, unless you entered into that arrangement, or that patent license -was granted, prior to 28 March 2007. - -Nothing in this License shall be construed as excluding or limiting any implied -license or other defenses to infringement that may otherwise be available to you -under applicable patent law. - -### 12. No Surrender of Others' Freedom - -If conditions are imposed on you (whether by court order, agreement or otherwise) -that contradict the conditions of this License, they do not excuse you from the -conditions of this License. If you cannot convey a covered work so as to satisfy -simultaneously your obligations under this License and any other pertinent -obligations, then as a consequence you may not convey it at all. For example, if you -agree to terms that obligate you to collect a royalty for further conveying from -those to whom you convey the Program, the only way you could satisfy both those terms -and this License would be to refrain entirely from conveying the Program. - -### 13. Use with the GNU Affero General Public License - -Notwithstanding any other provision of this License, you have permission to link or -combine any covered work with a work licensed under version 3 of the GNU Affero -General Public License into a single combined work, and to convey the resulting work. -The terms of this License will continue to apply to the part which is the covered -work, but the special requirements of the GNU Affero General Public License, section -13, concerning interaction through a network will apply to the combination as such. - -### 14. Revised Versions of this License - -The Free Software Foundation may publish revised and/or new versions of the GNU -General Public License from time to time. Such new versions will be similar in spirit -to the present version, but may differ in detail to address new problems or concerns. - -Each version is given a distinguishing version number. If the Program specifies that -a certain numbered version of the GNU General Public License “or any later -version” applies to it, you have the option of following the terms and -conditions either of that numbered version or of any later version published by the -Free Software Foundation. If the Program does not specify a version number of the GNU -General Public License, you may choose any version ever published by the Free -Software Foundation. - -If the Program specifies that a proxy can decide which future versions of the GNU -General Public License can be used, that proxy's public statement of acceptance of a -version permanently authorizes you to choose that version for the Program. - -Later license versions may give you additional or different permissions. However, no -additional obligations are imposed on any author or copyright holder as a result of -your choosing to follow a later version. - -### 15. Disclaimer of Warranty - -THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. -EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES -PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER -EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF -MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE -QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE -DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. - -### 16. Limitation of Liability - -IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY -COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS -PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, -INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE -PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE -OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE -WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE -POSSIBILITY OF SUCH DAMAGES. - -### 17. Interpretation of Sections 15 and 16 - -If the disclaimer of warranty and limitation of liability provided above cannot be -given local legal effect according to their terms, reviewing courts shall apply local -law that most closely approximates an absolute waiver of all civil liability in -connection with the Program, unless a warranty or assumption of liability accompanies -a copy of the Program in return for a fee. - -_END OF TERMS AND CONDITIONS_ - -## How to Apply These Terms to Your New Programs - -If you develop a new program, and you want it to be of the greatest possible use to -the public, the best way to achieve this is to make it free software which everyone -can redistribute and change under these terms. - -To do so, attach the following notices to the program. It is safest to attach them -to the start of each source file to most effectively state the exclusion of warranty; -and each file should have at least the “copyright” line and a pointer to -where the full notice is found. - - - Copyright (C) 2019 NULL - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -Also add information on how to contact you by electronic and paper mail. - -If the program does terminal interaction, make it output a short notice like this -when it starts in an interactive mode: - - concurve Copyright (C) 2019 NULL - This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type 'show c' for details. - -The hypothetical commands `show w` and `show c` should show the appropriate parts of -the General Public License. Of course, your program's commands might be different; -for a GUI interface, you would use an “about box”. - -You should also get your employer (if you work as a programmer) or school, if any, to -sign a “copyright disclaimer” for the program, if necessary. For more -information on this, and how to apply and follow the GNU GPL, see -<>. - -The GNU General Public License does not permit incorporating your program into -proprietary programs. If your program is a subroutine library, you may consider it -more useful to permit linking proprietary applications with the library. If this is -what you want to do, use the GNU Lesser General Public License instead of this -License. But first, please read -<>. diff --git a/NEWS.md b/NEWS.md index 0fceb9d..7e75748 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,20 +1,20 @@ # concurve 2.3.0 ## Major changes - -* `ggconcurve()` plots confidence (consonance) distributions, densities, and likelihood functions. +* `ggconcurve()` is now `ggcurve()`. +* `ggcurve()` plots confidence (consonance) distributions, densities, likelihood, and deviance functions. +* `plot_curve()` is now deprecated. Please use `ggcurve()` instead. * `curve_compare()` compares two functions and calculates the area between the curve. * `plot_compare() `allows two separate functions to be plotted and compared simultaneously. * `curve_table()` produces publication-ready tables of relevant statistics. -* `curve_boot()` uses bootstrapping to approximate the consonance functions. -* `curve_lik()` produces likelihood functions by transforming the objects from the `ProfileLikelihood` package. +* `curve_boot()` uses bootstrapping to approximate the consonance functions via the [`boot`](https://cran.r-project.org/package=boot) and [`bcaboot`](https://cran.r-project.org/package=bcaboot) packages. +* `curve_lik()` produces likelihood functions by transforming the objects from the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) package. ## Minor changes * All functions now provide progress on how long it will take to complete the task. * Interval widths are now provided as measures of precision. - # concurve 2.1.0 ## Major changes diff --git a/R/.DS_Store b/R/.DS_Store index 3f04d6afe12e22f235fcbc4d2360f3ac6e8d0a95..0ad4d397e9d3bb5c7ab7113a42c12b3dc1add4e9 100644 GIT binary patch delta 32 ocmZoMXfc@J&&a+pU^gQp`(_@dM#jxz9D*zp8!|VubNuB80G(V3h5!Hn delta 133 zcmZoMXfc@J&&atkU^gQp=Vl(JM#gj=hIAlIX2@sA1Cpf-MGR#OsSJ7yL7qAJ$w@i+ uNem1O0t^hywm@3rKNtX63=I4X1q?YrT_p_hMCe| +concurve | Graph Interval Functions ================ [![CRAN status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) @@ -11,37 +10,22 @@ concurve | Graph Interval Functions - - - - -* * * - ## Compare Interval Functions From Different Data/Studies - - - - + + + + * * * -## Export Tabled Results Easily For Word, Powerpoint, & LaTeX +## Export Tabled Results Easily For Word, Powerpoint, & TeX documents - +
    + +
    # Installation diff --git a/README.md b/README.md new file mode 100644 index 0000000..63082fe --- /dev/null +++ b/README.md @@ -0,0 +1,209 @@ + +# concurve | Graph Interval Functions + + + +[![CRAN +status](https://www.r-pkg.org/badges/version/concurve)](https://CRAN.R-project.org/package=concurve) +[![Travis build +status](https://travis-ci.org/Zadchow/concurve.svg?branch=master)](https://travis-ci.org/Zadchow/concurve) +[![Lifecycle: +maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) +[![](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran.r-project.org/package=concurve) +[![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve) +[![License: GPL +v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) + + +## Compare Interval Functions From Different Data/Studies + + + + + + +----- + +## Export Tabled Results Easily For Word, Powerpoint, & TeX documents + +
    + + + +
    + +# Installation + +## For R: + +### Install the Package From CRAN + +``` r +install.packages("concurve") +``` + +### Install the Developer Version + +``` r +library(devtools) +install_github("zadchow/concurve") +``` + +### Check out the [Examples](https://data.lesslikely.com/concurve/articles/examples.html). + +----- + +## For Stata: + +### Check out the [Article on Using Stata](https://data.lesslikely.com/concurve/articles/stata.html) for concurve. + +----- + +# Dependencies + + - ggplot2 + - metafor + - parallel + - MASS + - boot + - bcaboot + - compiler + - ProfileLikelihood + - pbmcapply + - rlang + - magrittr + - dplyr + - tidyr + - knitr + - flextable + - officer + - tibble + - survival + - survminer + - scales + +----- + +# Purpose + +> In particular, the usual 95% default forces the user’s focus onto +> parameter values that yield p \> 0.05, without regard to the trivial +> difference between (say) p = 0.06 and p = 0.04 (a difference not even +> worth a coin toss). To address this problem, we first note that a 95% +> interval estimate is only one of a number of arbitrary dichotomization +> of possibilities of parameter values (into either inside or outside of +> an interval). A more accurate picture of uncertainty is then obtained +> by examining intervals using other percentiles, e.g., +> proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, +> which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s +> \< 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or +> graph of all the P-values and S-values across a broad range of +> parameter values seems the clearest way to see how compatibility +> varies smoothly across the values. +> +> Graphs of P-values or their equivalent have been promoted for decades +> \[34, 56–59\], yet their adoption has been slight. Nonetheless, +> P-value and S-value graphing software is now available freely through +> several statistical packages \[60–62\]. A graph of the P-values p +> against possible parameter values allows one to see at a glance which +> parameter values are most compatible with the data under the +> background assumptions. This graph is known as the P-value function, +> or compatibility, consonance, or confidence curve \[34, 57, 58, 63, +> 64\]. Transforming the corresponding P-values in the graph to S-values +> produces an S-value (surprisal) function. +> +> Following the common (and important) warning that P-values are not +> hypothesis probabilities, we caution that the P-value graph is not a +> probability distribution: It shows compatibility of parameter values +> with the data, rather than plausibility or probability of those values +> given the data. This is not a subtle difference: compatibility is a +> much weaker condition than plausibility. Consider for example that +> complete fabrication of the data is always an explanation compatible +> with the data (and indeed has happened in some influential medical +> studies \[65\]), but in studies with many participants and authors +> involved in all aspects of data collection it becomes so implausible +> or improbable as to not even merit mention. We emphasize then that all +> the P-value ever addresses in a direct logical sense is compatibility; +> for hypothesis probabilities one must turn to Bayesian methods \[25\]. +> - [Chow & Greenland, 2019](https://arxiv.org/abs/1909.08579); +> [Greenland & Chow, 2019](https://arxiv.org/abs/1909.08583) + +----- + +> In addition to the overt statistical position, the p-value function +> also provides easily and accurately many of the familiar types of +> summary information: a **median estimate** of the parameter; a +> **one-sided test statistic** for a scalar parameter value at any +> chosen level; the related **power function**; a **lower confidence +> bound** at any level; an **upper confidence bound** at any level; and +> **confidence intervals** with chosen upper and lower confidence +> limits. The p value reports all the **common inference material**, but +> with **high accuracy, basic uniqueness, and wide generality**. +> +> From a scientific perspective, the **likelihood function** and +> **p-value function** provide the basis for scientific judgments by an +> investigator, and by other investigators who might have interest. **It +> thus replaces a blunt yes or no decision by an opportunity for +> appropriate informed judgment.**” - +> [Fraser, 2019](https://doi.org/10.1080/00031305.2018.1556735) + +----- + +> *Statistical software does not help you know what to compute, nor how +> to interpret the result. It does not offer to explain the assumptions +> behind methods, nor does it flag delicate or dubious assumptions. It +> does not warn you about multiplicity or p-hacking. It does not check +> whether you picked the hypothesis or analysis after looking at the +> data, nor track the number of analyses you tried before arriving at +> the one you sought to publish – another form of multiplicity. The more +> “powerful” and “user-friendly” the software is, the more it invites +> cargo-cult statistics*." - Stark & Saltelli, 2018 + +# References + +1. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid + Statistical Inference: Replace Confidence and Significance by + Compatibility and Surprise. [*arXiv:1909.08579 + \[stat.ME\]*.](https://arxiv.org/abs/1909.08579) 2019 +2. Greenland S, Chow ZR. To Aid Statistical Inference, Emphasize + Unconditional Descriptions of Statistics. [*arXiv:1909.08583 + \[stat.ME\]*.](https://arxiv.org/abs/1909.08583) 2019 +3. Poole C. Beyond the confidence interval. *Am J Public Health.* + 1987;77(2):195-199. +4. Sullivan KM, Foster DA. Use of the confidence interval + function.\_Epidemiology.\_ 1990;1(1):39-42. +5. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 2012. +6. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – + distribution estimator of a parameter. *arXiv \[mathST\]*. 2007. +7. Schweder T, Hjort NL. Confidence and Likelihood\*. *Scand J Stat.* + 2002;29(2):309-332. +8. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as + Descriptive Statistics: There is No Replication Crisis if We Don’t + Expect Replication. *Am Stat*. 2019 +9. Greenland S. Valid P-values Behave Exactly As They Should. Some + misleading criticisms of P-values and their resolution with + S-values. *Am Stat*. 2019;18(136). +10. Fraser DAS. The p-value Function and Statistical Inference. *Am + Stat*. 2019 +11. Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. + *Significance.* 2018;15(4):40-43. + +# Session info + + ## R version 3.6.1 (2019-07-05) + ## Platform: x86_64-apple-darwin15.6.0 (64-bit) + ## Running under: macOS Catalina 10.15.1 + ## + ## Matrix products: default + ## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib + ## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib + ## + ## locale: + ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 + ## + ## attached base packages: + ## [1] stats graphics grDevices utils methods base + ## + ## loaded via a namespace (and not attached): + ## [1] compiler_3.6.1 magrittr_1.5 tools_3.6.1 htmltools_0.4.0 yaml_2.2.0 Rcpp_1.0.3 stringi_1.4.3 + ## [8] rmarkdown_1.18 knitr_1.26 stringr_1.4.0 xfun_0.11 digest_0.6.23 rlang_0.4.2 evaluate_0.14 diff --git a/_pkgdown.yml b/_pkgdown.yml index 5438c4f..961a678 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,5 +1,7 @@ url: https://data.lesslikely.com/concurve/ +title: concurve + authors: Zad R. Chow: href: https://lesslikely.com/ @@ -7,6 +9,9 @@ authors: href: https://www.researchgate.net/profile/Andrew_Vigotsky template: + path: inst/templates + assets: inst/assets + default_assets: false params: bootswatch: paper docsearch: @@ -14,6 +19,7 @@ template: index_name: lesslikely-concurve navbar: + type: default structure: left: - home @@ -42,4 +48,4 @@ navbar: href: news/index.html github: icon: fa-github fa-lg - href: https://github.com/Zadchow/concurve + href: https://github.com/zadchow/concurve diff --git a/codemeta.json b/codemeta.json index 1769096..7b1923e 100644 --- a/codemeta.json +++ b/codemeta.json @@ -8,13 +8,13 @@ "description": "Allows one to compute consonance (confidence)\n intervals for various statistical tests along with their corresponding\n P-values, S-values, and likelihoods. The intervals can be plotted to\n create consonance, surprisal, and likelihood functions allowing one to\n see what effect sizes are compatible with the test model at various\n consonance levels rather than being limited to one interval estimate\n such as 95%. These methods are discussed by Poole C. (1987)\n , Schweder T, Hjort NL. (2002)\n , Singh K, Xie M, Strawderman WE. (2007)\n , Rothman KJ, Greenland S, Lash TL. (2008,\n ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019)\n , Greenland S. (2019)\n , Chow ZR, Greenland S. (2019)\n , and Greenland S, Chow ZR. (2019)\n .", "name": "concurve: Computes and Plots Compatibility (Confidence) Intervals,\n P-Values, S-Values, & Likelihood Intervals to Form Consonance,\n Surprisal, & Likelihood Functions", "date": "2019-11-24", - "codeRepository": "https://github.com/Zadchow/concurve", + "codeRepository": "https://github.com/zadchow/concurve", "relatedLink": [ "https://data.lesslikely.com/concurve/", "https://lesslikely.com/", "https://CRAN.R-project.org/package=concurve" ], - "issueTracker": "https://github.com/Zadchow/concurve/issues", + "issueTracker": "https://github.com/zadchow/concurve/issues", "license": "https://spdx.org/licenses/GPL-3.0", "version": "2.3.0", "programmingLanguage": { @@ -114,6 +114,18 @@ "url": "https://cran.r-project.org" }, "sameAs": "https://CRAN.R-project.org/package=rmarkdown" + }, + { + "@type": "SoftwareApplication", + "identifier": "Lock5Data", + "name": "Lock5Data", + "provider": { + "@id": "https://cran.r-project.org", + "@type": "Organization", + "name": "Comprehensive R Archive Network (CRAN)", + "url": "https://cran.r-project.org" + }, + "sameAs": "https://CRAN.R-project.org/package=Lock5Data" } ], "softwareRequirements": [ @@ -345,7 +357,7 @@ } ], "keywords": ["confidence", "compatibility", "consonance", "surprisal", "interval", "function", "curve"], - "fileSize": "3837.025KB", + "fileSize": "2225.323KB", "contIntegration": [ "https://travis-ci.org/Zadchow/concurve", "https://ci.appveyor.com/project/Zadchow/concurve", diff --git a/examples/Untitled.R b/examples/Untitled.R index f79a753..8667220 100644 --- a/examples/Untitled.R +++ b/examples/Untitled.R @@ -161,7 +161,7 @@ text(x = 1.25, y = 30, paste("99% CI:", -ggconcurve("consonance", randomframe, position = "pyramid", nullvalue = "present") +ggcurve("consonance", randomframe, position = "pyramid", nullvalue = "present") plot.concurve(type = "consonance", data = randomframe) library(microbenchmark) diff --git a/examples/examples.R b/examples/examples.R index cdde789..7b7aa36 100644 --- a/examples/examples.R +++ b/examples/examples.R @@ -19,9 +19,9 @@ tibble::tibble(intervalsdf[[1]]) (z <- curve_table(intervalsdf[[1]], format = "latex")) (z <- curve_table(intervalsdf[[1]], format = "image")) -(function1 <- ggconcurve(data = intervalsdf[[1]], type = "c")) -(function1 <- ggconcurve(data = intervalsdf[[1]], type = "s")) -(function1s <- ggconcurve(data = intervalsdf[[2]], type = "cdf")) +(function1 <- ggcurve(data = intervalsdf[[1]], type = "c")) +(function1 <- ggcurve(data = intervalsdf[[1]], type = "s")) +(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf")) options(mc.cores = 8L) @@ -48,7 +48,7 @@ model <- glm(GroupA2 ~ GroupB2, data = RandomData2) system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)) -(function2 <- ggconcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) +(function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) (curve_compare( data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", @@ -88,12 +88,11 @@ system.time(randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 1 lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000) -(ggconcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) - +(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000) -(ggconcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) +(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = FALSE)) (plot_compare( @@ -111,7 +110,7 @@ lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "rat plot_compare(data1 = lik1[[1]], data2 = lik2[[1]], type = "d", measure = "ratio", nullvalue = FALSE) -(function4 <- ggconcurve(randomframe[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +(function4 <- ggcurve(randomframe[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") ) @@ -145,8 +144,8 @@ rfun <- function(Xy) { system.time(x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 20000, steps = 1000)) -ggconcurve(data = x[[1]]) -ggconcurve(data = x[[3]]) +ggcurve(data = x[[1]]) +ggcurve(data = x[[3]]) plot_compare(x[[1]], x[[3]]) @@ -162,8 +161,8 @@ foo <- function(data, indices) { y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 200, steps = 1000) -ggconcurve(data = y[[1]]) -ggconcurve(data = y[[3]]) +ggcurve(data = y[[1]]) +ggcurve(data = y[[3]]) plot_compare(y[[1]], y[[3]]) @@ -179,8 +178,8 @@ func = function(data, index) { } z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 200, steps = 1000) -ggconcurve(data = z[[1]]) -ggconcurve(data = z[[2]], type = "cd") +ggcurve(data = z[[1]]) +ggcurve(data = z[[2]], type = "cd") # Meta-Analysis ----------------------------------------------------------- @@ -248,16 +247,16 @@ metaf2 <- curve_meta(res2) tibble::tibble(metaf2[[1]]) -(function5 <- ggconcurve(metaf[[1]], type = "c")) +(function5 <- ggcurve(metaf[[1]], type = "c")) -(function6 <- ggconcurve(metaf[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +(function6 <- ggcurve(metaf[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") ) -(function7 <- ggconcurve(metaf2[[1]], type = "c")) +(function7 <- ggcurve(metaf2[[1]], type = "c")) -(function8 <- ggconcurve(metaf2[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") +(function8 <- ggcurve(metaf2[[1]], type = "s", title = "Surprisal Function", subtitle = "Interval estimates are plotted to their corresponding S-values") ) (curve_compare(data1 = metaf[[1]], data2 = metaf2[[1]], type = "c", plot = TRUE, measure = "default") diff --git a/inst/WORDLIST b/inst/WORDLIST index d7cd958..fee2d03 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -8,6 +8,7 @@ BCa bcaboot boostrap cd +cdf DAS databrowser defaultis @@ -21,23 +22,25 @@ estiate eval flextable forvalues +frac functons -ggconcurve +ggcurve ggplot glm -GroupB Hjort insertedinto -intervalsdf kendall KJ knitr +leq levelinto Lifecycle +likelihoodm lm logarithmically lointerval magrittr +mathbf mathST metafor Metaprogramming @@ -50,6 +53,7 @@ pearson postfile powerpoint Powerpoint +ppt pptx ProfileLikelihood pvalue @@ -63,6 +67,7 @@ Saltelli Scand Schweder spearman +SSRI Stata Strawderman summarise @@ -79,6 +84,7 @@ Twoway typicallywhat upinterval wald +widehat xaxis Xie yxis diff --git a/inst/assets/bootstrap.css b/inst/assets/bootstrap.css new file mode 100644 index 0000000..fc8a203 --- /dev/null +++ b/inst/assets/bootstrap.css @@ -0,0 +1,7331 @@ +@import url("https://fonts.googleapis.com/css?family=Roboto:300,400,500,700"); +/*! + * bootswatch v3.4.1 + * Homepage: http://bootswatch.com + * Copyright 2012-2019 Thomas Park + * Licensed under MIT + * Based on Bootstrap +*/ +/*! + * Bootstrap v3.4.1 (https://getbootstrap.com/) + * Copyright 2011-2019 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ +/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ +html { + font-family: sans-serif; + -ms-text-size-adjust: 100%; + -webkit-text-size-adjust: 100%; +} +body { + margin: 0; +} +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +main, +menu, +nav, +section, +summary { + display: block; +} +audio, +canvas, +progress, +video { + display: inline-block; + vertical-align: baseline; +} +audio:not([controls]) { + display: none; + height: 0; +} +[hidden], +template { + display: none; 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input[type="button"], +input[type="reset"], +input[type="submit"] { + -webkit-appearance: button; + cursor: pointer; +} +button[disabled], +html input[disabled] { + cursor: default; +} +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} +input { + line-height: normal; +} +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + padding: 0; +} +input[type="number"]::-webkit-inner-spin-button, +input[type="number"]::-webkit-outer-spin-button { + height: auto; +} +input[type="search"] { + -webkit-appearance: textfield; + box-sizing: content-box; +} +input[type="search"]::-webkit-search-cancel-button, +input[type="search"]::-webkit-search-decoration { + -webkit-appearance: none; +} +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} +legend { + border: 0; + padding: 0; +} +textarea { + overflow: auto; +} +optgroup { + font-weight: bold; +} +table { + border-collapse: collapse; + border-spacing: 0; +} +td, +th { + padding: 0; +} +/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */ +@media print { + *, + *:before, + *:after { + color: #000 !important; + text-shadow: none !important; + background: transparent !important; + box-shadow: none !important; + } + a, + a:visited { + text-decoration: underline; + } + a[href]:after { + content: " (" attr(href) ")"; + } + abbr[title]:after { + content: " (" attr(title) ")"; + } + a[href^="#"]:after, + a[href^="javascript:"]:after { + content: ""; + } + pre, + blockquote { + border: 1px solid #999; + page-break-inside: avoid; + } + thead { + display: table-header-group; + } + tr, + img { + page-break-inside: avoid; + } + img { + max-width: 100% !important; + } + p, + h2, + h3 { + orphans: 3; + widows: 3; + } + h2, + h3 { + page-break-after: avoid; + } + .navbar { + display: none; + } + .btn > .caret, + .dropup > .btn > .caret { + border-top-color: #000 !important; + } + .label { + border: 1px solid #000; + } + .table { + border-collapse: collapse !important; + } + .table td, + .table th { + background-color: #fff !important; + } + .table-bordered th, + .table-bordered td { + border: 1px solid #ddd !important; + } +} +@font-face { + font-family: "Glyphicons Halflings"; + src: url("../fonts/glyphicons-halflings-regular.eot"); + src: url("../fonts/glyphicons-halflings-regular.eot?#iefix") format("embedded-opentype"), url("../fonts/glyphicons-halflings-regular.woff2") format("woff2"), url("../fonts/glyphicons-halflings-regular.woff") format("woff"), url("../fonts/glyphicons-halflings-regular.ttf") format("truetype"), url("../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular") format("svg"); +} +.glyphicon { + position: relative; + top: 1px; + display: inline-block; + font-family: "Glyphicons Halflings"; + font-style: normal; + font-weight: 400; + line-height: 1; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +.glyphicon-asterisk:before { + content: "\002a"; +} +.glyphicon-plus:before { + content: "\002b"; +} +.glyphicon-euro:before, +.glyphicon-eur:before { + content: "\20ac"; +} +.glyphicon-minus:before { + content: "\2212"; +} +.glyphicon-cloud:before { + content: "\2601"; +} +.glyphicon-envelope:before { + content: "\2709"; +} +.glyphicon-pencil:before { + content: "\270f"; +} +.glyphicon-glass:before { + content: "\e001"; +} +.glyphicon-music:before { + content: "\e002"; +} +.glyphicon-search:before { + content: "\e003"; +} +.glyphicon-heart:before { + content: "\e005"; +} +.glyphicon-star:before { + content: "\e006"; +} +.glyphicon-star-empty:before { + content: "\e007"; +} +.glyphicon-user:before { + content: "\e008"; +} +.glyphicon-film:before { + content: "\e009"; +} +.glyphicon-th-large:before { + content: "\e010"; +} +.glyphicon-th:before { + content: "\e011"; +} +.glyphicon-th-list:before { + content: "\e012"; +} +.glyphicon-ok:before { + content: "\e013"; +} +.glyphicon-remove:before { + content: "\e014"; +} +.glyphicon-zoom-in:before { + content: "\e015"; +} +.glyphicon-zoom-out:before { + content: "\e016"; +} +.glyphicon-off:before { + content: "\e017"; +} +.glyphicon-signal:before { + content: "\e018"; +} +.glyphicon-cog:before { + content: "\e019"; +} +.glyphicon-trash:before { + content: "\e020"; +} +.glyphicon-home:before { + content: "\e021"; +} +.glyphicon-file:before { + content: "\e022"; +} +.glyphicon-time:before { + content: "\e023"; +} +.glyphicon-road:before { + content: "\e024"; +} +.glyphicon-download-alt:before { + content: "\e025"; +} +.glyphicon-download:before { + content: "\e026"; +} +.glyphicon-upload:before { + content: "\e027"; +} +.glyphicon-inbox:before { + content: "\e028"; +} +.glyphicon-play-circle:before { + content: "\e029"; +} +.glyphicon-repeat:before { + content: "\e030"; +} +.glyphicon-refresh:before { + content: "\e031"; +} +.glyphicon-list-alt:before { + content: "\e032"; +} +.glyphicon-lock:before { + content: "\e033"; +} +.glyphicon-flag:before { + content: "\e034"; +} +.glyphicon-headphones:before { + content: "\e035"; +} +.glyphicon-volume-off:before { + content: "\e036"; +} +.glyphicon-volume-down:before { + content: "\e037"; +} +.glyphicon-volume-up:before { + content: "\e038"; +} +.glyphicon-qrcode:before { + content: "\e039"; +} +.glyphicon-barcode:before { + content: "\e040"; +} +.glyphicon-tag:before { + content: "\e041"; +} +.glyphicon-tags:before { + content: "\e042"; +} +.glyphicon-book:before { + content: "\e043"; +} +.glyphicon-bookmark:before { + content: "\e044"; +} +.glyphicon-print:before { + content: "\e045"; +} +.glyphicon-camera:before { + content: "\e046"; +} +.glyphicon-font:before { + content: "\e047"; +} +.glyphicon-bold:before { + content: "\e048"; +} +.glyphicon-italic:before { + content: "\e049"; +} +.glyphicon-text-height:before { + content: "\e050"; +} +.glyphicon-text-width:before { + content: "\e051"; +} +.glyphicon-align-left:before { + content: "\e052"; +} +.glyphicon-align-center:before { + content: "\e053"; +} +.glyphicon-align-right:before { + content: "\e054"; +} +.glyphicon-align-justify:before { + content: "\e055"; +} +.glyphicon-list:before { + content: "\e056"; +} +.glyphicon-indent-left:before { + content: "\e057"; +} +.glyphicon-indent-right:before { + content: "\e058"; +} +.glyphicon-facetime-video:before { + content: "\e059"; +} +.glyphicon-picture:before { + content: "\e060"; +} +.glyphicon-map-marker:before { + content: "\e062"; +} +.glyphicon-adjust:before { + content: "\e063"; +} +.glyphicon-tint:before { + content: "\e064"; +} +.glyphicon-edit:before { + content: "\e065"; +} +.glyphicon-share:before { + content: "\e066"; +} +.glyphicon-check:before { + content: "\e067"; +} +.glyphicon-move:before { + content: "\e068"; +} +.glyphicon-step-backward:before { + content: "\e069"; +} +.glyphicon-fast-backward:before { + content: "\e070"; +} +.glyphicon-backward:before { + content: "\e071"; +} +.glyphicon-play:before { + content: "\e072"; +} +.glyphicon-pause:before { + content: "\e073"; +} +.glyphicon-stop:before { + content: "\e074"; +} +.glyphicon-forward:before { + content: "\e075"; +} +.glyphicon-fast-forward:before { + content: "\e076"; +} +.glyphicon-step-forward:before { + content: "\e077"; +} +.glyphicon-eject:before { + content: "\e078"; +} +.glyphicon-chevron-left:before { + content: "\e079"; +} +.glyphicon-chevron-right:before { + content: "\e080"; +} +.glyphicon-plus-sign:before { + content: "\e081"; +} +.glyphicon-minus-sign:before { + content: "\e082"; +} +.glyphicon-remove-sign:before { + content: "\e083"; +} +.glyphicon-ok-sign:before { + content: "\e084"; +} +.glyphicon-question-sign:before { + content: "\e085"; +} +.glyphicon-info-sign:before { + content: "\e086"; +} +.glyphicon-screenshot:before { + content: "\e087"; +} +.glyphicon-remove-circle:before { + content: "\e088"; +} +.glyphicon-ok-circle:before { + content: "\e089"; +} +.glyphicon-ban-circle:before { + content: "\e090"; +} +.glyphicon-arrow-left:before { + content: "\e091"; +} +.glyphicon-arrow-right:before { + content: "\e092"; +} +.glyphicon-arrow-up:before { + content: "\e093"; +} +.glyphicon-arrow-down:before { + content: "\e094"; +} +.glyphicon-share-alt:before { + content: "\e095"; +} +.glyphicon-resize-full:before { + content: "\e096"; +} +.glyphicon-resize-small:before { + content: "\e097"; +} +.glyphicon-exclamation-sign:before { + content: "\e101"; +} +.glyphicon-gift:before { + content: "\e102"; +} +.glyphicon-leaf:before { + content: "\e103"; +} +.glyphicon-fire:before { + content: "\e104"; +} +.glyphicon-eye-open:before { + content: "\e105"; +} +.glyphicon-eye-close:before { + content: "\e106"; +} +.glyphicon-warning-sign:before { + content: "\e107"; +} +.glyphicon-plane:before { + content: "\e108"; +} +.glyphicon-calendar:before { + content: "\e109"; +} +.glyphicon-random:before { + content: "\e110"; +} +.glyphicon-comment:before { + content: "\e111"; +} +.glyphicon-magnet:before { + content: "\e112"; +} +.glyphicon-chevron-up:before { + content: "\e113"; +} +.glyphicon-chevron-down:before { + content: "\e114"; +} +.glyphicon-retweet:before { + content: "\e115"; +} +.glyphicon-shopping-cart:before { + content: "\e116"; +} +.glyphicon-folder-close:before { + content: "\e117"; +} +.glyphicon-folder-open:before { + content: "\e118"; +} +.glyphicon-resize-vertical:before { + content: "\e119"; +} +.glyphicon-resize-horizontal:before { + content: "\e120"; +} +.glyphicon-hdd:before { + content: "\e121"; +} +.glyphicon-bullhorn:before { + content: "\e122"; +} +.glyphicon-bell:before { + content: "\e123"; +} +.glyphicon-certificate:before { + content: "\e124"; +} +.glyphicon-thumbs-up:before { + content: "\e125"; +} +.glyphicon-thumbs-down:before { + content: "\e126"; +} +.glyphicon-hand-right:before { + content: "\e127"; +} +.glyphicon-hand-left:before { + content: "\e128"; +} +.glyphicon-hand-up:before { + content: "\e129"; +} +.glyphicon-hand-down:before { + content: "\e130"; +} +.glyphicon-circle-arrow-right:before { + content: "\e131"; +} +.glyphicon-circle-arrow-left:before { + content: "\e132"; +} +.glyphicon-circle-arrow-up:before { + content: "\e133"; +} +.glyphicon-circle-arrow-down:before { + content: "\e134"; +} +.glyphicon-globe:before { + content: "\e135"; +} +.glyphicon-wrench:before { + content: "\e136"; +} +.glyphicon-tasks:before { + content: "\e137"; +} +.glyphicon-filter:before { + content: "\e138"; +} +.glyphicon-briefcase:before { + content: "\e139"; +} +.glyphicon-fullscreen:before { + content: "\e140"; +} +.glyphicon-dashboard:before { + content: "\e141"; +} +.glyphicon-paperclip:before { + content: "\e142"; +} +.glyphicon-heart-empty:before { + content: "\e143"; +} +.glyphicon-link:before { + content: "\e144"; +} +.glyphicon-phone:before { + content: "\e145"; +} +.glyphicon-pushpin:before { + content: "\e146"; +} +.glyphicon-usd:before { + content: "\e148"; +} +.glyphicon-gbp:before { + content: "\e149"; +} +.glyphicon-sort:before { + content: "\e150"; +} +.glyphicon-sort-by-alphabet:before { + content: "\e151"; +} +.glyphicon-sort-by-alphabet-alt:before { + content: "\e152"; +} +.glyphicon-sort-by-order:before { + content: "\e153"; +} +.glyphicon-sort-by-order-alt:before { + content: "\e154"; +} +.glyphicon-sort-by-attributes:before { + content: "\e155"; +} +.glyphicon-sort-by-attributes-alt:before { + content: "\e156"; +} +.glyphicon-unchecked:before { + content: "\e157"; +} +.glyphicon-expand:before { + content: "\e158"; +} +.glyphicon-collapse-down:before { + content: "\e159"; +} +.glyphicon-collapse-up:before { + content: "\e160"; +} +.glyphicon-log-in:before { + content: "\e161"; +} +.glyphicon-flash:before { + content: "\e162"; +} +.glyphicon-log-out:before { + content: "\e163"; +} +.glyphicon-new-window:before { + content: "\e164"; +} +.glyphicon-record:before { + content: "\e165"; +} +.glyphicon-save:before { + content: "\e166"; +} +.glyphicon-open:before { + content: "\e167"; +} +.glyphicon-saved:before { + content: "\e168"; +} +.glyphicon-import:before { + content: "\e169"; +} +.glyphicon-export:before { + content: "\e170"; +} +.glyphicon-send:before { + content: "\e171"; +} +.glyphicon-floppy-disk:before { + content: "\e172"; +} +.glyphicon-floppy-saved:before { + content: "\e173"; +} +.glyphicon-floppy-remove:before { + content: "\e174"; +} +.glyphicon-floppy-save:before { + content: "\e175"; +} +.glyphicon-floppy-open:before { + content: "\e176"; +} +.glyphicon-credit-card:before { + content: "\e177"; +} +.glyphicon-transfer:before { + content: "\e178"; +} +.glyphicon-cutlery:before { + content: "\e179"; +} +.glyphicon-header:before { + content: "\e180"; +} +.glyphicon-compressed:before { + content: "\e181"; +} +.glyphicon-earphone:before { + content: "\e182"; +} +.glyphicon-phone-alt:before { + content: "\e183"; +} +.glyphicon-tower:before { + content: "\e184"; +} +.glyphicon-stats:before { + content: "\e185"; +} +.glyphicon-sd-video:before { + content: "\e186"; +} +.glyphicon-hd-video:before { + content: "\e187"; +} +.glyphicon-subtitles:before { + content: "\e188"; +} +.glyphicon-sound-stereo:before { + content: "\e189"; +} +.glyphicon-sound-dolby:before { + content: "\e190"; +} +.glyphicon-sound-5-1:before { + content: "\e191"; +} +.glyphicon-sound-6-1:before { + content: "\e192"; +} +.glyphicon-sound-7-1:before { + content: "\e193"; +} +.glyphicon-copyright-mark:before { + content: "\e194"; +} +.glyphicon-registration-mark:before { + content: "\e195"; +} +.glyphicon-cloud-download:before { + content: "\e197"; +} +.glyphicon-cloud-upload:before { + content: "\e198"; +} +.glyphicon-tree-conifer:before { + content: "\e199"; +} +.glyphicon-tree-deciduous:before { + content: "\e200"; +} +.glyphicon-cd:before { + content: "\e201"; +} +.glyphicon-save-file:before { + content: "\e202"; +} +.glyphicon-open-file:before { + content: "\e203"; +} +.glyphicon-level-up:before { + content: "\e204"; +} +.glyphicon-copy:before { + content: "\e205"; +} +.glyphicon-paste:before { + content: "\e206"; +} +.glyphicon-alert:before { + content: "\e209"; +} +.glyphicon-equalizer:before { + content: "\e210"; +} +.glyphicon-king:before { + content: "\e211"; +} +.glyphicon-queen:before { + content: "\e212"; +} +.glyphicon-pawn:before { + content: "\e213"; +} +.glyphicon-bishop:before { + content: "\e214"; +} +.glyphicon-knight:before { + content: "\e215"; +} +.glyphicon-baby-formula:before { + content: "\e216"; +} +.glyphicon-tent:before { + content: "\26fa"; +} +.glyphicon-blackboard:before { + content: "\e218"; +} +.glyphicon-bed:before { + content: "\e219"; +} +.glyphicon-apple:before { + content: "\f8ff"; +} +.glyphicon-erase:before { + content: "\e221"; +} +.glyphicon-hourglass:before { + content: "\231b"; +} +.glyphicon-lamp:before { + content: "\e223"; +} +.glyphicon-duplicate:before { + content: "\e224"; +} +.glyphicon-piggy-bank:before { + content: "\e225"; +} +.glyphicon-scissors:before { + content: "\e226"; +} +.glyphicon-bitcoin:before { + content: "\e227"; +} +.glyphicon-btc:before { + content: "\e227"; +} +.glyphicon-xbt:before { + content: "\e227"; +} +.glyphicon-yen:before { + content: "\00a5"; +} +.glyphicon-jpy:before { + content: "\00a5"; +} +.glyphicon-ruble:before { + content: "\20bd"; +} +.glyphicon-rub:before { + content: "\20bd"; +} +.glyphicon-scale:before { + content: "\e230"; +} +.glyphicon-ice-lolly:before { + content: "\e231"; +} +.glyphicon-ice-lolly-tasted:before { + content: "\e232"; +} +.glyphicon-education:before { + content: "\e233"; +} +.glyphicon-option-horizontal:before { + content: "\e234"; +} +.glyphicon-option-vertical:before { + content: "\e235"; +} +.glyphicon-menu-hamburger:before { + content: "\e236"; +} +.glyphicon-modal-window:before { + content: "\e237"; +} +.glyphicon-oil:before { + content: "\e238"; +} +.glyphicon-grain:before { + content: "\e239"; +} +.glyphicon-sunglasses:before { + content: "\e240"; +} +.glyphicon-text-size:before { + content: "\e241"; +} +.glyphicon-text-color:before { + content: "\e242"; +} +.glyphicon-text-background:before { + content: "\e243"; +} +.glyphicon-object-align-top:before { + content: "\e244"; +} +.glyphicon-object-align-bottom:before { + content: "\e245"; +} +.glyphicon-object-align-horizontal:before { + content: "\e246"; +} +.glyphicon-object-align-left:before { + content: "\e247"; +} +.glyphicon-object-align-vertical:before { + content: "\e248"; +} +.glyphicon-object-align-right:before { + content: "\e249"; +} +.glyphicon-triangle-right:before { + content: "\e250"; +} +.glyphicon-triangle-left:before { + content: "\e251"; +} +.glyphicon-triangle-bottom:before { + content: "\e252"; +} +.glyphicon-triangle-top:before { + content: "\e253"; +} +.glyphicon-console:before { + content: "\e254"; +} +.glyphicon-superscript:before { + content: "\e255"; +} +.glyphicon-subscript:before { + content: "\e256"; +} +.glyphicon-menu-left:before { + content: "\e257"; +} +.glyphicon-menu-right:before { + content: "\e258"; +} +.glyphicon-menu-down:before { + content: "\e259"; +} +.glyphicon-menu-up:before { + content: "\e260"; +} +* { + box-sizing: border-box; +} +*:before, +*:after { + box-sizing: border-box; +} +html { + font-size: 10px; + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} +body { + font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + line-height: 1.846; + color: #666666; + background-color: #ffffff; +} +input, +button, +select, +textarea { + font-family: inherit; + font-size: inherit; + line-height: inherit; +} +a { + color: #2196f3; + text-decoration: none; +} +a:hover, +a:focus { + color: #0a6ebd; + text-decoration: underline; +} +a:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +figure { + margin: 0; +} +img { + vertical-align: middle; +} +.img-responsive, +.thumbnail > img, +.thumbnail a > img, +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + display: block; + max-width: 100%; + height: auto; +} +.img-rounded { + border-radius: 3px; +} +.img-thumbnail { + padding: 4px; + line-height: 1.846; + background-color: #ffffff; + border: 1px solid #dddddd; + border-radius: 3px; + transition: all 0.2s ease-in-out; + display: inline-block; + max-width: 100%; + height: auto; +} +.img-circle { + border-radius: 50%; +} +hr { + margin-top: 23px; + margin-bottom: 23px; + border: 0; + border-top: 1px solid #eeeeee; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} +[role="button"] { + cursor: pointer; +} +h1, +h2, +h3, +h4, +h5, +h6, +.h1, +.h2, +.h3, +.h4, +.h5, +.h6 { + font-family: inherit; + font-weight: 400; + line-height: 1.1; + color: #444444; +} +h1 small, +h2 small, +h3 small, +h4 small, +h5 small, +h6 small, +.h1 small, +.h2 small, +.h3 small, +.h4 small, +.h5 small, +.h6 small, +h1 .small, +h2 .small, +h3 .small, +h4 .small, +h5 .small, +h6 .small, +.h1 .small, +.h2 .small, +.h3 .small, +.h4 .small, +.h5 .small, +.h6 .small { + font-weight: 400; + line-height: 1; + color: #bbbbbb; +} +h1, +.h1, +h2, +.h2, +h3, +.h3 { + margin-top: 23px; + margin-bottom: 11.5px; +} +h1 small, +.h1 small, +h2 small, +.h2 small, +h3 small, +.h3 small, +h1 .small, +.h1 .small, +h2 .small, +.h2 .small, +h3 .small, +.h3 .small { + font-size: 65%; +} +h4, +.h4, +h5, +.h5, +h6, +.h6 { + margin-top: 11.5px; + margin-bottom: 11.5px; +} +h4 small, +.h4 small, +h5 small, +.h5 small, +h6 small, +.h6 small, +h4 .small, +.h4 .small, +h5 .small, +.h5 .small, +h6 .small, +.h6 .small { + font-size: 75%; +} +h1, +.h1 { + font-size: 56px; +} +h2, +.h2 { + font-size: 45px; +} +h3, +.h3 { + font-size: 34px; +} +h4, +.h4 { + font-size: 24px; +} +h5, +.h5 { + font-size: 20px; +} +h6, +.h6 { + font-size: 14px; +} +p { + margin: 0 0 11.5px; +} +.lead { + margin-bottom: 23px; + font-size: 14px; + font-weight: 300; + line-height: 1.4; +} +@media (min-width: 768px) { + .lead { + font-size: 19.5px; + } +} +small, +.small { + font-size: 92%; +} +mark, +.mark { + padding: .2em; + background-color: #ffe0b2; +} +.text-left { + text-align: left; +} +.text-right { + text-align: right; +} +.text-center { + text-align: center; +} +.text-justify { + text-align: justify; +} +.text-nowrap { + white-space: nowrap; +} +.text-lowercase { + text-transform: lowercase; +} +.text-uppercase { + text-transform: uppercase; +} +.text-capitalize { + text-transform: capitalize; +} +.text-muted { + color: #bbbbbb; +} +.text-primary { + color: #2196f3; +} +a.text-primary:hover, +a.text-primary:focus { + color: #0c7cd5; +} +.text-success { + color: #4caf50; +} +a.text-success:hover, +a.text-success:focus { + color: #3d8b40; +} +.text-info { + color: #9c27b0; +} +a.text-info:hover, +a.text-info:focus { + color: #771e86; +} +.text-warning { + color: #ff9800; +} +a.text-warning:hover, +a.text-warning:focus { + color: #cc7a00; +} +.text-danger { + color: #e51c23; +} +a.text-danger:hover, +a.text-danger:focus { + color: #b9151b; +} +.bg-primary { + color: #fff; + background-color: #2196f3; +} +a.bg-primary:hover, +a.bg-primary:focus { + background-color: #0c7cd5; +} +.bg-success { + background-color: #dff0d8; +} +a.bg-success:hover, +a.bg-success:focus { + background-color: #c1e2b3; +} +.bg-info { + background-color: #e1bee7; +} +a.bg-info:hover, +a.bg-info:focus { + background-color: #d099d9; +} +.bg-warning { + background-color: #ffe0b2; +} +a.bg-warning:hover, +a.bg-warning:focus { + background-color: #ffcb7f; +} +.bg-danger { + background-color: #f9bdbb; +} +a.bg-danger:hover, +a.bg-danger:focus { + background-color: #f5908c; +} +.page-header { + padding-bottom: 10.5px; + margin: 46px 0 23px; + border-bottom: 1px solid #eeeeee; +} +ul, +ol { + margin-top: 0; + margin-bottom: 11.5px; +} +ul ul, +ol ul, +ul ol, +ol ol { + margin-bottom: 0; +} +.list-unstyled { + padding-left: 0; + list-style: none; +} +.list-inline { + padding-left: 0; + list-style: none; + margin-left: -5px; +} +.list-inline > li { + display: inline-block; + padding-right: 5px; + padding-left: 5px; +} +dl { + margin-top: 0; + margin-bottom: 23px; +} +dt, +dd { + line-height: 1.846; +} +dt { + font-weight: 700; +} +dd { + margin-left: 0; +} +@media (min-width: 768px) { + .dl-horizontal dt { + float: left; + width: 160px; + clear: left; + text-align: right; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + } + .dl-horizontal dd { + margin-left: 180px; + } +} +abbr[title], +abbr[data-original-title] { + cursor: help; +} +.initialism { + font-size: 90%; + text-transform: uppercase; +} +blockquote { + padding: 11.5px 23px; + margin: 0 0 23px; + font-size: 16.25px; + border-left: 5px solid #eeeeee; +} +blockquote p:last-child, +blockquote ul:last-child, +blockquote ol:last-child { + margin-bottom: 0; +} +blockquote footer, +blockquote small, +blockquote .small { + display: block; + font-size: 80%; + line-height: 1.846; + color: #bbbbbb; +} +blockquote footer:before, +blockquote small:before, +blockquote .small:before { + content: "\2014 \00A0"; +} +.blockquote-reverse, +blockquote.pull-right { + padding-right: 15px; + padding-left: 0; + text-align: right; + border-right: 5px solid #eeeeee; + border-left: 0; +} +.blockquote-reverse footer:before, +blockquote.pull-right footer:before, +.blockquote-reverse small:before, +blockquote.pull-right small:before, +.blockquote-reverse .small:before, +blockquote.pull-right .small:before { + content: ""; +} +.blockquote-reverse footer:after, +blockquote.pull-right footer:after, +.blockquote-reverse small:after, +blockquote.pull-right small:after, +.blockquote-reverse .small:after, +blockquote.pull-right .small:after { + content: "\00A0 \2014"; +} +address { + margin-bottom: 23px; + font-style: normal; + line-height: 1.846; +} +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; +} +code { + padding: 2px 4px; + font-size: 90%; + color: #c7254e; + background-color: #f9f2f4; + border-radius: 3px; +} +kbd { + padding: 2px 4px; + font-size: 90%; + color: #ffffff; + background-color: #333333; + border-radius: 3px; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); +} +kbd kbd { + padding: 0; + font-size: 100%; + font-weight: 700; + box-shadow: none; +} +pre { + display: block; + padding: 11px; + margin: 0 0 11.5px; + font-size: 12px; + line-height: 1.846; + color: #212121; + word-break: break-all; + word-wrap: break-word; + background-color: #f5f5f5; + border: 1px solid #cccccc; + border-radius: 3px; +} +pre code { + padding: 0; + font-size: inherit; + color: inherit; + white-space: pre-wrap; + background-color: transparent; + border-radius: 0; +} +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} +.container { + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +@media (min-width: 768px) { + .container { + width: 750px; + } +} +@media (min-width: 992px) { + .container { + width: 970px; + } +} +@media (min-width: 1200px) { + .container { + width: 1170px; + } +} +.container-fluid { + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +.row { + margin-right: -15px; + margin-left: -15px; +} +.row-no-gutters { + margin-right: 0; + margin-left: 0; +} +.row-no-gutters [class*="col-"] { + padding-right: 0; + padding-left: 0; +} +.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { + position: relative; + min-height: 1px; + padding-right: 15px; + padding-left: 15px; +} +.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { + float: left; +} +.col-xs-12 { + width: 100%; +} +.col-xs-11 { + width: 91.66666667%; +} +.col-xs-10 { + width: 83.33333333%; +} +.col-xs-9 { + width: 75%; +} +.col-xs-8 { + width: 66.66666667%; +} +.col-xs-7 { + width: 58.33333333%; +} +.col-xs-6 { + width: 50%; +} +.col-xs-5 { + width: 41.66666667%; +} +.col-xs-4 { + width: 33.33333333%; +} +.col-xs-3 { + width: 25%; +} +.col-xs-2 { + width: 16.66666667%; +} +.col-xs-1 { + width: 8.33333333%; +} +.col-xs-pull-12 { + right: 100%; +} +.col-xs-pull-11 { + right: 91.66666667%; +} +.col-xs-pull-10 { + right: 83.33333333%; +} +.col-xs-pull-9 { + right: 75%; +} +.col-xs-pull-8 { + right: 66.66666667%; +} +.col-xs-pull-7 { + right: 58.33333333%; +} +.col-xs-pull-6 { + right: 50%; +} +.col-xs-pull-5 { + right: 41.66666667%; +} +.col-xs-pull-4 { + right: 33.33333333%; +} +.col-xs-pull-3 { + right: 25%; +} +.col-xs-pull-2 { + right: 16.66666667%; +} +.col-xs-pull-1 { + right: 8.33333333%; +} +.col-xs-pull-0 { + right: auto; +} +.col-xs-push-12 { + left: 100%; +} +.col-xs-push-11 { + left: 91.66666667%; +} +.col-xs-push-10 { + left: 83.33333333%; +} +.col-xs-push-9 { + left: 75%; +} +.col-xs-push-8 { + left: 66.66666667%; +} +.col-xs-push-7 { + left: 58.33333333%; +} +.col-xs-push-6 { + left: 50%; +} +.col-xs-push-5 { + left: 41.66666667%; +} +.col-xs-push-4 { + left: 33.33333333%; +} +.col-xs-push-3 { + left: 25%; +} +.col-xs-push-2 { + left: 16.66666667%; +} +.col-xs-push-1 { + left: 8.33333333%; +} +.col-xs-push-0 { + left: auto; +} +.col-xs-offset-12 { + margin-left: 100%; +} +.col-xs-offset-11 { + margin-left: 91.66666667%; +} +.col-xs-offset-10 { + margin-left: 83.33333333%; +} +.col-xs-offset-9 { + margin-left: 75%; +} +.col-xs-offset-8 { + margin-left: 66.66666667%; +} +.col-xs-offset-7 { + margin-left: 58.33333333%; +} +.col-xs-offset-6 { + margin-left: 50%; +} +.col-xs-offset-5 { + margin-left: 41.66666667%; +} +.col-xs-offset-4 { + margin-left: 33.33333333%; +} +.col-xs-offset-3 { + margin-left: 25%; +} +.col-xs-offset-2 { + margin-left: 16.66666667%; +} +.col-xs-offset-1 { + margin-left: 8.33333333%; +} +.col-xs-offset-0 { + margin-left: 0%; +} +@media (min-width: 768px) { + .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { + float: left; + } + .col-sm-12 { + width: 100%; + } + .col-sm-11 { + width: 91.66666667%; + } + .col-sm-10 { + width: 83.33333333%; + } + .col-sm-9 { + width: 75%; + } + .col-sm-8 { + width: 66.66666667%; + } + .col-sm-7 { + width: 58.33333333%; + } + .col-sm-6 { + width: 50%; + } + .col-sm-5 { + width: 41.66666667%; + } + .col-sm-4 { + width: 33.33333333%; + } + .col-sm-3 { + width: 25%; + } + .col-sm-2 { + width: 16.66666667%; + } + .col-sm-1 { + width: 8.33333333%; + } + .col-sm-pull-12 { + right: 100%; + } + .col-sm-pull-11 { + right: 91.66666667%; + } + .col-sm-pull-10 { + right: 83.33333333%; + } + .col-sm-pull-9 { + right: 75%; + } + .col-sm-pull-8 { + right: 66.66666667%; + } + .col-sm-pull-7 { + right: 58.33333333%; + } + .col-sm-pull-6 { + right: 50%; + } + .col-sm-pull-5 { + right: 41.66666667%; + } + .col-sm-pull-4 { + right: 33.33333333%; + } + .col-sm-pull-3 { + right: 25%; + } + .col-sm-pull-2 { + right: 16.66666667%; + } + .col-sm-pull-1 { + right: 8.33333333%; + } + .col-sm-pull-0 { + right: auto; + } + .col-sm-push-12 { + left: 100%; + } + .col-sm-push-11 { + left: 91.66666667%; + } + .col-sm-push-10 { + left: 83.33333333%; + } + .col-sm-push-9 { + left: 75%; + } + .col-sm-push-8 { + left: 66.66666667%; + } + .col-sm-push-7 { + left: 58.33333333%; + } + .col-sm-push-6 { + left: 50%; + } + .col-sm-push-5 { + left: 41.66666667%; + } + .col-sm-push-4 { + left: 33.33333333%; + } + .col-sm-push-3 { + left: 25%; + } + .col-sm-push-2 { + left: 16.66666667%; + } + .col-sm-push-1 { + left: 8.33333333%; + } + .col-sm-push-0 { + left: auto; + } + .col-sm-offset-12 { + margin-left: 100%; + } + .col-sm-offset-11 { + margin-left: 91.66666667%; + } + .col-sm-offset-10 { + margin-left: 83.33333333%; + } + .col-sm-offset-9 { + margin-left: 75%; + } + .col-sm-offset-8 { + margin-left: 66.66666667%; + } + .col-sm-offset-7 { + margin-left: 58.33333333%; + } + .col-sm-offset-6 { + margin-left: 50%; + } + .col-sm-offset-5 { + margin-left: 41.66666667%; + } + .col-sm-offset-4 { + margin-left: 33.33333333%; + } + .col-sm-offset-3 { + margin-left: 25%; + } + .col-sm-offset-2 { + margin-left: 16.66666667%; + } + .col-sm-offset-1 { + margin-left: 8.33333333%; + } + .col-sm-offset-0 { + margin-left: 0%; + } +} +@media (min-width: 992px) { + .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { + float: left; + } + .col-md-12 { + width: 100%; + } + .col-md-11 { + width: 91.66666667%; + } + .col-md-10 { + width: 83.33333333%; + } + .col-md-9 { + width: 75%; + } + .col-md-8 { + width: 66.66666667%; + } + .col-md-7 { + width: 58.33333333%; + } + .col-md-6 { + width: 50%; + } + .col-md-5 { + width: 41.66666667%; + } + .col-md-4 { + width: 33.33333333%; + } + .col-md-3 { + width: 25%; + } + .col-md-2 { + width: 16.66666667%; + } + .col-md-1 { + width: 8.33333333%; + } + .col-md-pull-12 { + right: 100%; + } + .col-md-pull-11 { + right: 91.66666667%; + } + .col-md-pull-10 { + right: 83.33333333%; + } + .col-md-pull-9 { + right: 75%; + } + .col-md-pull-8 { + right: 66.66666667%; + } + .col-md-pull-7 { + right: 58.33333333%; + } + .col-md-pull-6 { + right: 50%; + } + .col-md-pull-5 { + right: 41.66666667%; + } + .col-md-pull-4 { + right: 33.33333333%; + } + .col-md-pull-3 { + right: 25%; + } + .col-md-pull-2 { + right: 16.66666667%; + } + .col-md-pull-1 { + right: 8.33333333%; + } + .col-md-pull-0 { + right: auto; + } + .col-md-push-12 { + left: 100%; + } + .col-md-push-11 { + left: 91.66666667%; + } + .col-md-push-10 { + left: 83.33333333%; + } + .col-md-push-9 { + left: 75%; + } + .col-md-push-8 { + left: 66.66666667%; + } + .col-md-push-7 { + left: 58.33333333%; + } + .col-md-push-6 { + left: 50%; + } + .col-md-push-5 { + left: 41.66666667%; + } + .col-md-push-4 { + left: 33.33333333%; + } + .col-md-push-3 { + left: 25%; + } + .col-md-push-2 { + left: 16.66666667%; + } + .col-md-push-1 { + left: 8.33333333%; + } + .col-md-push-0 { + left: auto; + } + .col-md-offset-12 { + margin-left: 100%; + } + .col-md-offset-11 { + margin-left: 91.66666667%; + } + .col-md-offset-10 { + margin-left: 83.33333333%; + } + .col-md-offset-9 { + margin-left: 75%; + } + .col-md-offset-8 { + margin-left: 66.66666667%; + } + .col-md-offset-7 { + margin-left: 58.33333333%; + } + .col-md-offset-6 { + margin-left: 50%; + } + .col-md-offset-5 { + margin-left: 41.66666667%; + } + .col-md-offset-4 { + margin-left: 33.33333333%; + } + .col-md-offset-3 { + margin-left: 25%; + } + .col-md-offset-2 { + margin-left: 16.66666667%; + } + .col-md-offset-1 { + margin-left: 8.33333333%; + } + .col-md-offset-0 { + margin-left: 0%; + } +} +@media (min-width: 1200px) { + .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { + float: left; + } + .col-lg-12 { + width: 100%; + } + .col-lg-11 { + width: 91.66666667%; + } + .col-lg-10 { + width: 83.33333333%; + } + .col-lg-9 { + width: 75%; + } + .col-lg-8 { + width: 66.66666667%; + } + .col-lg-7 { + width: 58.33333333%; + } + .col-lg-6 { + width: 50%; + } + .col-lg-5 { + width: 41.66666667%; + } + .col-lg-4 { + width: 33.33333333%; + } + .col-lg-3 { + width: 25%; + } + .col-lg-2 { + width: 16.66666667%; + } + .col-lg-1 { + width: 8.33333333%; + } + .col-lg-pull-12 { + right: 100%; + } + .col-lg-pull-11 { + right: 91.66666667%; + } + .col-lg-pull-10 { + right: 83.33333333%; + } + .col-lg-pull-9 { + right: 75%; + } + .col-lg-pull-8 { + right: 66.66666667%; + } + .col-lg-pull-7 { + right: 58.33333333%; + } + .col-lg-pull-6 { + right: 50%; + } + .col-lg-pull-5 { + right: 41.66666667%; + } + .col-lg-pull-4 { + right: 33.33333333%; + } + .col-lg-pull-3 { + right: 25%; + } + .col-lg-pull-2 { + right: 16.66666667%; + } + .col-lg-pull-1 { + right: 8.33333333%; + } + .col-lg-pull-0 { + right: auto; + } + .col-lg-push-12 { + left: 100%; + } + .col-lg-push-11 { + left: 91.66666667%; + } + .col-lg-push-10 { + left: 83.33333333%; + } + .col-lg-push-9 { + left: 75%; + } + .col-lg-push-8 { + left: 66.66666667%; + } + .col-lg-push-7 { + left: 58.33333333%; + } + .col-lg-push-6 { + left: 50%; + } + .col-lg-push-5 { + left: 41.66666667%; + } + .col-lg-push-4 { + left: 33.33333333%; + } + .col-lg-push-3 { + left: 25%; + } + .col-lg-push-2 { + left: 16.66666667%; + } + .col-lg-push-1 { + left: 8.33333333%; + } + .col-lg-push-0 { + left: auto; + } + .col-lg-offset-12 { + margin-left: 100%; + } + .col-lg-offset-11 { + margin-left: 91.66666667%; + } + .col-lg-offset-10 { + margin-left: 83.33333333%; + } + .col-lg-offset-9 { + margin-left: 75%; + } + .col-lg-offset-8 { + margin-left: 66.66666667%; + } + .col-lg-offset-7 { + margin-left: 58.33333333%; + } + .col-lg-offset-6 { + margin-left: 50%; + } + .col-lg-offset-5 { + margin-left: 41.66666667%; + } + .col-lg-offset-4 { + margin-left: 33.33333333%; + } + .col-lg-offset-3 { + margin-left: 25%; + } + .col-lg-offset-2 { + margin-left: 16.66666667%; + } + .col-lg-offset-1 { + margin-left: 8.33333333%; + } + .col-lg-offset-0 { + margin-left: 0%; + } +} +table { + background-color: transparent; +} +table col[class*="col-"] { + position: static; + display: table-column; + float: none; +} +table td[class*="col-"], +table th[class*="col-"] { + position: static; + display: table-cell; + float: none; +} +caption { + padding-top: 8px; + padding-bottom: 8px; + color: #bbbbbb; + text-align: left; +} +th { + text-align: left; +} +.table { + width: 100%; + max-width: 100%; + margin-bottom: 23px; +} +.table > thead > tr > th, +.table > tbody > tr > th, +.table > tfoot > tr > th, +.table > thead > tr > td, +.table > tbody > tr > td, +.table > tfoot > tr > td { + padding: 8px; + line-height: 1.846; + vertical-align: top; + border-top: 1px solid #dddddd; +} +.table > thead > tr > th { + vertical-align: bottom; + border-bottom: 2px solid #dddddd; +} +.table > caption + thead > tr:first-child > th, +.table > colgroup + thead > tr:first-child > th, +.table > thead:first-child > tr:first-child > th, +.table > caption + thead > tr:first-child > td, +.table > colgroup + thead > tr:first-child > td, +.table > thead:first-child > tr:first-child > td { + border-top: 0; +} +.table > tbody + tbody { + border-top: 2px solid #dddddd; +} +.table .table { + background-color: #ffffff; +} +.table-condensed > thead > tr > th, +.table-condensed > tbody > tr > th, +.table-condensed > tfoot > tr > th, +.table-condensed > thead > tr > td, +.table-condensed > tbody > tr > td, +.table-condensed > tfoot > tr > td { + padding: 5px; +} +.table-bordered { + border: 1px solid #dddddd; +} +.table-bordered > thead > tr > th, +.table-bordered > tbody > tr > th, +.table-bordered > tfoot > tr > th, +.table-bordered > thead > tr > td, +.table-bordered > tbody > tr > td, +.table-bordered > tfoot > tr > td { + border: 1px solid #dddddd; +} +.table-bordered > thead > tr > th, +.table-bordered > thead > tr > td { + border-bottom-width: 2px; +} +.table-striped > tbody > tr:nth-of-type(odd) { + background-color: #f9f9f9; +} +.table-hover > tbody > tr:hover { + background-color: #f5f5f5; +} +.table > thead > tr > td.active, +.table > tbody > tr > td.active, +.table > tfoot > tr > td.active, +.table > thead > tr > th.active, +.table > tbody > tr > th.active, +.table > tfoot > tr > th.active, +.table > thead > tr.active > td, +.table > tbody > tr.active > td, +.table > tfoot > tr.active > td, +.table > thead > tr.active > th, +.table > tbody > tr.active > th, +.table > tfoot > tr.active > th { + background-color: #f5f5f5; +} +.table-hover > tbody > tr > td.active:hover, +.table-hover > tbody > tr > th.active:hover, +.table-hover > tbody > tr.active:hover > td, +.table-hover > tbody > tr:hover > .active, +.table-hover > tbody > tr.active:hover > th { + background-color: #e8e8e8; +} +.table > thead > tr > td.success, +.table > tbody > tr > td.success, +.table > tfoot > tr > td.success, +.table > thead > tr > th.success, +.table > tbody > tr > th.success, +.table > tfoot > tr > th.success, +.table > thead > tr.success > td, +.table > tbody > tr.success > td, +.table > tfoot > tr.success > td, +.table > thead > tr.success > th, +.table > tbody > tr.success > th, +.table > tfoot > tr.success > th { + background-color: #dff0d8; +} +.table-hover > tbody > tr > td.success:hover, +.table-hover > tbody > tr > th.success:hover, +.table-hover > tbody > tr.success:hover > td, +.table-hover > tbody > tr:hover > .success, +.table-hover > tbody > tr.success:hover > th { + background-color: #d0e9c6; +} +.table > thead > tr > td.info, +.table > tbody > tr > td.info, +.table > tfoot > tr > td.info, +.table > thead > tr > th.info, +.table > tbody > tr > th.info, +.table > tfoot > tr > th.info, +.table > thead > tr.info > td, +.table > tbody > tr.info > td, +.table > tfoot > tr.info > td, +.table > thead > tr.info > th, +.table > tbody > tr.info > th, +.table > tfoot > tr.info > th { + background-color: #e1bee7; +} +.table-hover > tbody > tr > td.info:hover, +.table-hover > tbody > tr > th.info:hover, +.table-hover > tbody > tr.info:hover > td, +.table-hover > tbody > tr:hover > .info, +.table-hover > tbody > tr.info:hover > th { + background-color: #d8abe0; +} +.table > thead > tr > td.warning, +.table > tbody > tr > td.warning, +.table > tfoot > tr > td.warning, +.table > thead > tr > th.warning, +.table > tbody > tr > th.warning, +.table > tfoot > tr > th.warning, +.table > thead > tr.warning > td, +.table > tbody > tr.warning > td, +.table > tfoot > tr.warning > td, +.table > thead > tr.warning > th, +.table > tbody > tr.warning > th, +.table > tfoot > tr.warning > th { + background-color: #ffe0b2; +} +.table-hover > tbody > tr > td.warning:hover, +.table-hover > tbody > tr > th.warning:hover, +.table-hover > tbody > tr.warning:hover > td, +.table-hover > tbody > tr:hover > .warning, +.table-hover > tbody > tr.warning:hover > th { + background-color: #ffd699; +} +.table > thead > tr > td.danger, +.table > tbody > tr > td.danger, +.table > tfoot > tr > td.danger, +.table > thead > tr > th.danger, +.table > tbody > tr > th.danger, +.table > tfoot > tr > th.danger, +.table > thead > tr.danger > td, +.table > tbody > tr.danger > td, +.table > tfoot > tr.danger > td, +.table > thead > tr.danger > th, +.table > tbody > tr.danger > th, +.table > tfoot > tr.danger > th { + background-color: #f9bdbb; +} +.table-hover > tbody > tr > td.danger:hover, +.table-hover > tbody > tr > th.danger:hover, +.table-hover > tbody > tr.danger:hover > td, +.table-hover > tbody > tr:hover > .danger, +.table-hover > tbody > tr.danger:hover > th { + background-color: #f7a6a4; +} +.table-responsive { + min-height: .01%; + overflow-x: auto; +} +@media screen and (max-width: 767px) { + .table-responsive { + width: 100%; + margin-bottom: 17.25px; + overflow-y: hidden; + -ms-overflow-style: -ms-autohiding-scrollbar; + border: 1px solid #dddddd; + } + .table-responsive > .table { + margin-bottom: 0; + } + .table-responsive > .table > thead > tr > th, + .table-responsive > .table > tbody > tr > th, + .table-responsive > .table > tfoot > tr > th, + .table-responsive > .table > thead > tr > td, + .table-responsive > .table > tbody > tr > td, + .table-responsive > .table > tfoot > tr > td { + white-space: nowrap; + } + .table-responsive > .table-bordered { + border: 0; + } + .table-responsive > .table-bordered > thead > tr > th:first-child, + .table-responsive > .table-bordered > tbody > tr > th:first-child, + .table-responsive > .table-bordered > tfoot > tr > th:first-child, + .table-responsive > .table-bordered > thead > tr > td:first-child, + .table-responsive > .table-bordered > tbody > tr > td:first-child, + .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; + } + .table-responsive > .table-bordered > thead > tr > th:last-child, + .table-responsive > .table-bordered > tbody > tr > th:last-child, + .table-responsive > .table-bordered > tfoot > tr > th:last-child, + .table-responsive > .table-bordered > thead > tr > td:last-child, + .table-responsive > .table-bordered > tbody > tr > td:last-child, + .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; + } + .table-responsive > .table-bordered > tbody > tr:last-child > th, + .table-responsive > .table-bordered > tfoot > tr:last-child > th, + .table-responsive > .table-bordered > tbody > tr:last-child > td, + .table-responsive > .table-bordered > tfoot > tr:last-child > td { + border-bottom: 0; + } +} +fieldset { + min-width: 0; + padding: 0; + margin: 0; + border: 0; +} +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 23px; + font-size: 19.5px; + line-height: inherit; + color: #212121; + border: 0; + border-bottom: 1px solid #e5e5e5; +} +label { + display: inline-block; + max-width: 100%; + margin-bottom: 5px; + font-weight: 700; +} +input[type="search"] { + box-sizing: border-box; + -webkit-appearance: none; + appearance: none; +} +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + line-height: normal; +} +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"].disabled, +input[type="checkbox"].disabled, +fieldset[disabled] input[type="radio"], +fieldset[disabled] input[type="checkbox"] { + cursor: not-allowed; +} +input[type="file"] { + display: block; +} +input[type="range"] { + display: block; + width: 100%; +} +select[multiple], +select[size] { + height: auto; +} +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +output { + display: block; + padding-top: 7px; + font-size: 13px; + line-height: 1.846; + color: #666666; +} +.form-control { + display: block; + width: 100%; + height: 37px; + padding: 6px 16px; + font-size: 13px; + line-height: 1.846; + color: #666666; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 3px; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; +} +.form-control:focus { + border-color: #66afe9; + outline: 0; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6); +} +.form-control::-moz-placeholder { + color: #bbbbbb; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #bbbbbb; +} +.form-control::-webkit-input-placeholder { + color: #bbbbbb; +} +.form-control::-ms-expand { + background-color: transparent; + border: 0; +} +.form-control[disabled], +.form-control[readonly], +fieldset[disabled] .form-control { + background-color: transparent; + opacity: 1; +} +.form-control[disabled], +fieldset[disabled] .form-control { + cursor: not-allowed; +} +textarea.form-control { + height: auto; +} +@media screen and (-webkit-min-device-pixel-ratio: 0) { + input[type="date"].form-control, + input[type="time"].form-control, + input[type="datetime-local"].form-control, + input[type="month"].form-control { + line-height: 37px; + } + input[type="date"].input-sm, + input[type="time"].input-sm, + input[type="datetime-local"].input-sm, + input[type="month"].input-sm, + .input-group-sm input[type="date"], + .input-group-sm input[type="time"], + .input-group-sm input[type="datetime-local"], + .input-group-sm input[type="month"] { + line-height: 30px; + } + input[type="date"].input-lg, + input[type="time"].input-lg, + input[type="datetime-local"].input-lg, + input[type="month"].input-lg, + .input-group-lg input[type="date"], + .input-group-lg input[type="time"], + .input-group-lg input[type="datetime-local"], + .input-group-lg input[type="month"] { + line-height: 45px; + } +} +.form-group { + margin-bottom: 15px; +} +.radio, +.checkbox { + position: relative; + display: block; + margin-top: 10px; + margin-bottom: 10px; +} +.radio.disabled label, +.checkbox.disabled label, +fieldset[disabled] .radio label, +fieldset[disabled] .checkbox label { + cursor: not-allowed; +} +.radio label, +.checkbox label { + min-height: 23px; + padding-left: 20px; + margin-bottom: 0; + font-weight: 400; + cursor: pointer; +} +.radio input[type="radio"], +.radio-inline input[type="radio"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + position: absolute; + margin-top: 4px \9; + margin-left: -20px; +} +.radio + .radio, +.checkbox + .checkbox { + margin-top: -5px; +} +.radio-inline, +.checkbox-inline { + position: relative; + display: inline-block; + padding-left: 20px; + margin-bottom: 0; + font-weight: 400; + vertical-align: middle; + cursor: pointer; +} +.radio-inline.disabled, +.checkbox-inline.disabled, +fieldset[disabled] .radio-inline, +fieldset[disabled] .checkbox-inline { + cursor: not-allowed; +} +.radio-inline + .radio-inline, +.checkbox-inline + .checkbox-inline { + margin-top: 0; + margin-left: 10px; +} +.form-control-static { + min-height: 36px; + padding-top: 7px; + padding-bottom: 7px; + margin-bottom: 0; +} +.form-control-static.input-lg, +.form-control-static.input-sm { + padding-right: 0; + padding-left: 0; +} +.input-sm { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +select.input-sm { + height: 30px; + line-height: 30px; +} +textarea.input-sm, +select[multiple].input-sm { + height: auto; +} +.form-group-sm .form-control { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.form-group-sm select.form-control { + height: 30px; + line-height: 30px; +} +.form-group-sm textarea.form-control, +.form-group-sm select[multiple].form-control { + height: auto; +} +.form-group-sm .form-control-static { + height: 30px; + min-height: 35px; + padding: 6px 10px; + font-size: 12px; + line-height: 1.5; +} +.input-lg { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +select.input-lg { + height: 45px; + line-height: 45px; +} +textarea.input-lg, +select[multiple].input-lg { + height: auto; +} +.form-group-lg .form-control { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +.form-group-lg select.form-control { + height: 45px; + line-height: 45px; +} +.form-group-lg textarea.form-control, +.form-group-lg select[multiple].form-control { + height: auto; +} +.form-group-lg .form-control-static { + height: 45px; + min-height: 40px; + padding: 11px 16px; + font-size: 17px; + line-height: 1.3333333; +} +.has-feedback { + position: relative; +} +.has-feedback .form-control { + padding-right: 46.25px; +} +.form-control-feedback { + position: absolute; + top: 0; + right: 0; + z-index: 2; + display: block; + width: 37px; + height: 37px; + line-height: 37px; + text-align: center; + pointer-events: none; +} +.input-lg + .form-control-feedback, +.input-group-lg + .form-control-feedback, +.form-group-lg .form-control + .form-control-feedback { + width: 45px; + height: 45px; + line-height: 45px; +} +.input-sm + .form-control-feedback, +.input-group-sm + .form-control-feedback, +.form-group-sm .form-control + .form-control-feedback { + width: 30px; + height: 30px; + line-height: 30px; +} +.has-success .help-block, +.has-success .control-label, +.has-success .radio, +.has-success .checkbox, +.has-success .radio-inline, +.has-success .checkbox-inline, +.has-success.radio label, +.has-success.checkbox label, +.has-success.radio-inline label, +.has-success.checkbox-inline label { + color: #4caf50; +} +.has-success .form-control { + border-color: #4caf50; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-success .form-control:focus { + border-color: #3d8b40; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #92cf94; +} +.has-success .input-group-addon { + color: #4caf50; + background-color: #dff0d8; + border-color: #4caf50; +} +.has-success .form-control-feedback { + color: #4caf50; +} +.has-warning .help-block, +.has-warning .control-label, +.has-warning .radio, +.has-warning .checkbox, +.has-warning .radio-inline, +.has-warning .checkbox-inline, +.has-warning.radio label, +.has-warning.checkbox label, +.has-warning.radio-inline label, +.has-warning.checkbox-inline label { + color: #ff9800; +} +.has-warning .form-control { + border-color: #ff9800; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-warning .form-control:focus { + border-color: #cc7a00; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ffc166; +} +.has-warning .input-group-addon { + color: #ff9800; + background-color: #ffe0b2; + border-color: #ff9800; +} +.has-warning .form-control-feedback { + color: #ff9800; +} +.has-error .help-block, +.has-error .control-label, +.has-error .radio, +.has-error .checkbox, +.has-error .radio-inline, +.has-error .checkbox-inline, +.has-error.radio label, +.has-error.checkbox label, +.has-error.radio-inline label, +.has-error.checkbox-inline label { + color: #e51c23; +} +.has-error .form-control { + border-color: #e51c23; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-error .form-control:focus { + border-color: #b9151b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ef787c; +} +.has-error .input-group-addon { + color: #e51c23; + background-color: #f9bdbb; + border-color: #e51c23; +} +.has-error .form-control-feedback { + color: #e51c23; +} +.has-feedback label ~ .form-control-feedback { + top: 28px; +} +.has-feedback label.sr-only ~ .form-control-feedback { + top: 0; +} +.help-block { + display: block; + margin-top: 5px; + margin-bottom: 10px; + color: #a6a6a6; +} +@media (min-width: 768px) { + .form-inline .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .form-inline .form-control-static { + display: inline-block; + } + .form-inline .input-group { + display: inline-table; + vertical-align: middle; + } + .form-inline .input-group .input-group-addon, + .form-inline .input-group .input-group-btn, + .form-inline .input-group .form-control { + width: auto; + } + .form-inline .input-group > .form-control { + width: 100%; + } + .form-inline .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio, + .form-inline .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio label, + .form-inline .checkbox label { + padding-left: 0; + } + .form-inline .radio input[type="radio"], + .form-inline .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .form-inline .has-feedback .form-control-feedback { + top: 0; + } +} +.form-horizontal .radio, +.form-horizontal .checkbox, +.form-horizontal .radio-inline, +.form-horizontal .checkbox-inline { + padding-top: 7px; + margin-top: 0; + margin-bottom: 0; +} +.form-horizontal .radio, +.form-horizontal .checkbox { + min-height: 30px; +} +.form-horizontal .form-group { + margin-right: -15px; + margin-left: -15px; +} +@media (min-width: 768px) { + .form-horizontal .control-label { + padding-top: 7px; + margin-bottom: 0; + text-align: right; + } +} +.form-horizontal .has-feedback .form-control-feedback { + right: 15px; +} +@media (min-width: 768px) { + .form-horizontal .form-group-lg .control-label { + padding-top: 11px; + font-size: 17px; + } +} +@media (min-width: 768px) { + .form-horizontal .form-group-sm .control-label { + padding-top: 6px; + font-size: 12px; + } +} +.btn { + display: inline-block; + margin-bottom: 0; + font-weight: normal; + text-align: center; + white-space: nowrap; + vertical-align: middle; + -ms-touch-action: manipulation; + touch-action: manipulation; + cursor: pointer; + background-image: none; + border: 1px solid transparent; + padding: 6px 16px; + font-size: 13px; + line-height: 1.846; + border-radius: 3px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} +.btn:focus, +.btn:active:focus, +.btn.active:focus, +.btn.focus, +.btn:active.focus, +.btn.active.focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +.btn:hover, +.btn:focus, +.btn.focus { + color: #444444; + text-decoration: none; +} +.btn:active, +.btn.active { + background-image: none; + outline: 0; + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn.disabled, +.btn[disabled], +fieldset[disabled] .btn { + cursor: not-allowed; + filter: alpha(opacity=65); + opacity: 0.65; + box-shadow: none; +} +a.btn.disabled, +fieldset[disabled] a.btn { + pointer-events: none; +} +.btn-default { + color: #444444; + background-color: #ffffff; + border-color: transparent; +} +.btn-default:focus, +.btn-default.focus { + color: #444444; + background-color: #e6e6e6; + border-color: rgba(0, 0, 0, 0); +} +.btn-default:hover { + color: #444444; + background-color: #e6e6e6; + border-color: rgba(0, 0, 0, 0); +} +.btn-default:active, +.btn-default.active, +.open > .dropdown-toggle.btn-default { + color: #444444; + background-color: #e6e6e6; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-default:active:hover, +.btn-default.active:hover, +.open > .dropdown-toggle.btn-default:hover, +.btn-default:active:focus, +.btn-default.active:focus, +.open > .dropdown-toggle.btn-default:focus, +.btn-default:active.focus, +.btn-default.active.focus, +.open > .dropdown-toggle.btn-default.focus { + color: #444444; + background-color: #d4d4d4; + border-color: rgba(0, 0, 0, 0); +} +.btn-default.disabled:hover, +.btn-default[disabled]:hover, +fieldset[disabled] .btn-default:hover, +.btn-default.disabled:focus, +.btn-default[disabled]:focus, +fieldset[disabled] .btn-default:focus, +.btn-default.disabled.focus, +.btn-default[disabled].focus, +fieldset[disabled] .btn-default.focus { + background-color: #ffffff; + border-color: transparent; +} +.btn-default .badge { + color: #ffffff; + background-color: #444444; +} +.btn-primary { + color: #ffffff; + background-color: #2196f3; + border-color: transparent; +} +.btn-primary:focus, +.btn-primary.focus { + color: #ffffff; + background-color: #0c7cd5; + border-color: rgba(0, 0, 0, 0); +} +.btn-primary:hover { + color: #ffffff; + background-color: #0c7cd5; + border-color: rgba(0, 0, 0, 0); +} +.btn-primary:active, +.btn-primary.active, +.open > .dropdown-toggle.btn-primary { + color: #ffffff; + background-color: #0c7cd5; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-primary:active:hover, +.btn-primary.active:hover, +.open > .dropdown-toggle.btn-primary:hover, +.btn-primary:active:focus, +.btn-primary.active:focus, +.open > .dropdown-toggle.btn-primary:focus, +.btn-primary:active.focus, +.btn-primary.active.focus, +.open > .dropdown-toggle.btn-primary.focus { + color: #ffffff; + background-color: #0a68b4; + border-color: rgba(0, 0, 0, 0); +} +.btn-primary.disabled:hover, +.btn-primary[disabled]:hover, +fieldset[disabled] .btn-primary:hover, +.btn-primary.disabled:focus, +.btn-primary[disabled]:focus, +fieldset[disabled] .btn-primary:focus, +.btn-primary.disabled.focus, +.btn-primary[disabled].focus, +fieldset[disabled] .btn-primary.focus { + background-color: #2196f3; + border-color: transparent; +} +.btn-primary .badge { + color: #2196f3; + background-color: #ffffff; +} +.btn-success { + color: #ffffff; + background-color: #4caf50; + border-color: transparent; +} +.btn-success:focus, +.btn-success.focus { + color: #ffffff; + background-color: #3d8b40; + border-color: rgba(0, 0, 0, 0); +} +.btn-success:hover { + color: #ffffff; + background-color: #3d8b40; + border-color: rgba(0, 0, 0, 0); +} +.btn-success:active, +.btn-success.active, +.open > .dropdown-toggle.btn-success { + color: #ffffff; + background-color: #3d8b40; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-success:active:hover, +.btn-success.active:hover, +.open > .dropdown-toggle.btn-success:hover, +.btn-success:active:focus, +.btn-success.active:focus, +.open > .dropdown-toggle.btn-success:focus, +.btn-success:active.focus, +.btn-success.active.focus, +.open > .dropdown-toggle.btn-success.focus { + color: #ffffff; + background-color: #327334; + border-color: rgba(0, 0, 0, 0); +} +.btn-success.disabled:hover, +.btn-success[disabled]:hover, +fieldset[disabled] .btn-success:hover, +.btn-success.disabled:focus, +.btn-success[disabled]:focus, +fieldset[disabled] .btn-success:focus, +.btn-success.disabled.focus, +.btn-success[disabled].focus, +fieldset[disabled] .btn-success.focus { + background-color: #4caf50; + border-color: transparent; +} +.btn-success .badge { + color: #4caf50; + background-color: #ffffff; +} +.btn-info { + color: #ffffff; + background-color: #9c27b0; + border-color: transparent; +} +.btn-info:focus, +.btn-info.focus { + color: #ffffff; + background-color: #771e86; + border-color: rgba(0, 0, 0, 0); +} +.btn-info:hover { + color: #ffffff; + background-color: #771e86; + border-color: rgba(0, 0, 0, 0); +} +.btn-info:active, +.btn-info.active, +.open > .dropdown-toggle.btn-info { + color: #ffffff; + background-color: #771e86; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-info:active:hover, +.btn-info.active:hover, +.open > .dropdown-toggle.btn-info:hover, +.btn-info:active:focus, +.btn-info.active:focus, +.open > .dropdown-toggle.btn-info:focus, +.btn-info:active.focus, +.btn-info.active.focus, +.open > .dropdown-toggle.btn-info.focus { + color: #ffffff; + background-color: #5d1769; + border-color: rgba(0, 0, 0, 0); +} +.btn-info.disabled:hover, +.btn-info[disabled]:hover, +fieldset[disabled] .btn-info:hover, +.btn-info.disabled:focus, +.btn-info[disabled]:focus, +fieldset[disabled] .btn-info:focus, +.btn-info.disabled.focus, +.btn-info[disabled].focus, +fieldset[disabled] .btn-info.focus { + background-color: #9c27b0; + border-color: transparent; +} +.btn-info .badge { + color: #9c27b0; + background-color: #ffffff; +} +.btn-warning { + color: #ffffff; + background-color: #ff9800; + border-color: transparent; +} +.btn-warning:focus, +.btn-warning.focus { + color: #ffffff; + background-color: #cc7a00; + border-color: rgba(0, 0, 0, 0); +} +.btn-warning:hover { + color: #ffffff; + background-color: #cc7a00; + border-color: rgba(0, 0, 0, 0); +} +.btn-warning:active, +.btn-warning.active, +.open > .dropdown-toggle.btn-warning { + color: #ffffff; + background-color: #cc7a00; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-warning:active:hover, +.btn-warning.active:hover, +.open > .dropdown-toggle.btn-warning:hover, +.btn-warning:active:focus, +.btn-warning.active:focus, +.open > .dropdown-toggle.btn-warning:focus, +.btn-warning:active.focus, +.btn-warning.active.focus, +.open > .dropdown-toggle.btn-warning.focus { + color: #ffffff; + background-color: #a86400; + border-color: rgba(0, 0, 0, 0); +} +.btn-warning.disabled:hover, +.btn-warning[disabled]:hover, +fieldset[disabled] .btn-warning:hover, +.btn-warning.disabled:focus, +.btn-warning[disabled]:focus, +fieldset[disabled] .btn-warning:focus, +.btn-warning.disabled.focus, +.btn-warning[disabled].focus, +fieldset[disabled] .btn-warning.focus { + background-color: #ff9800; + border-color: transparent; +} +.btn-warning .badge { + color: #ff9800; + background-color: #ffffff; +} +.btn-danger { + color: #ffffff; + background-color: #e51c23; + border-color: transparent; +} +.btn-danger:focus, +.btn-danger.focus { + color: #ffffff; + background-color: #b9151b; + border-color: rgba(0, 0, 0, 0); +} +.btn-danger:hover { + color: #ffffff; + background-color: #b9151b; + border-color: rgba(0, 0, 0, 0); +} +.btn-danger:active, +.btn-danger.active, +.open > .dropdown-toggle.btn-danger { + color: #ffffff; + background-color: #b9151b; + background-image: none; + border-color: rgba(0, 0, 0, 0); +} +.btn-danger:active:hover, +.btn-danger.active:hover, +.open > .dropdown-toggle.btn-danger:hover, +.btn-danger:active:focus, +.btn-danger.active:focus, +.open > .dropdown-toggle.btn-danger:focus, +.btn-danger:active.focus, +.btn-danger.active.focus, +.open > .dropdown-toggle.btn-danger.focus { + color: #ffffff; + background-color: #991216; + border-color: rgba(0, 0, 0, 0); +} +.btn-danger.disabled:hover, +.btn-danger[disabled]:hover, +fieldset[disabled] .btn-danger:hover, +.btn-danger.disabled:focus, +.btn-danger[disabled]:focus, +fieldset[disabled] .btn-danger:focus, +.btn-danger.disabled.focus, +.btn-danger[disabled].focus, +fieldset[disabled] .btn-danger.focus { + background-color: #e51c23; + border-color: transparent; +} +.btn-danger .badge { + color: #e51c23; + background-color: #ffffff; +} +.btn-link { + font-weight: 400; + color: #2196f3; + border-radius: 0; +} +.btn-link, +.btn-link:active, +.btn-link.active, +.btn-link[disabled], +fieldset[disabled] .btn-link { + background-color: transparent; + box-shadow: none; +} +.btn-link, +.btn-link:hover, +.btn-link:focus, +.btn-link:active { + border-color: transparent; +} +.btn-link:hover, +.btn-link:focus { + color: #0a6ebd; + text-decoration: underline; + background-color: transparent; +} +.btn-link[disabled]:hover, +fieldset[disabled] .btn-link:hover, +.btn-link[disabled]:focus, +fieldset[disabled] .btn-link:focus { + color: #bbbbbb; + text-decoration: none; +} +.btn-lg, +.btn-group-lg > .btn { + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +.btn-sm, +.btn-group-sm > .btn { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.btn-xs, +.btn-group-xs > .btn { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.btn-block { + display: block; + width: 100%; +} +.btn-block + .btn-block { + margin-top: 5px; +} +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} +.fade { + opacity: 0; + transition: opacity 0.15s linear; +} +.fade.in { + opacity: 1; +} +.collapse { + display: none; +} +.collapse.in { + display: block; +} +tr.collapse.in { + display: table-row; +} +tbody.collapse.in { + display: table-row-group; +} +.collapsing { + position: relative; + height: 0; + overflow: hidden; + transition-property: height, visibility; + transition-duration: 0.35s; + transition-timing-function: ease; +} +.caret { + display: inline-block; + width: 0; + height: 0; + margin-left: 2px; + vertical-align: middle; + border-top: 4px dashed; + border-top: 4px solid \9; + border-right: 4px solid transparent; + border-left: 4px solid transparent; +} +.dropup, +.dropdown { + position: relative; +} +.dropdown-toggle:focus { + outline: 0; +} +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + font-size: 13px; + text-align: left; + list-style: none; + background-color: #ffffff; + background-clip: padding-box; + border: 1px solid #cccccc; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 3px; + box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); +} +.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.dropdown-menu .divider { + height: 1px; + margin: 10.5px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.dropdown-menu > li > a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: 400; + line-height: 1.846; + color: #666666; + white-space: nowrap; +} +.dropdown-menu > li > a:hover, +.dropdown-menu > li > a:focus { + color: #141414; + text-decoration: none; + background-color: #eeeeee; +} +.dropdown-menu > .active > a, +.dropdown-menu > .active > a:hover, +.dropdown-menu > .active > a:focus { + color: #ffffff; + text-decoration: none; + background-color: #2196f3; + outline: 0; +} +.dropdown-menu > .disabled > a, +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + color: #bbbbbb; +} +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + text-decoration: none; + cursor: not-allowed; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); +} +.open > .dropdown-menu { + display: block; +} +.open > a { + outline: 0; +} +.dropdown-menu-right { + right: 0; + left: auto; +} +.dropdown-menu-left { + right: auto; + left: 0; +} +.dropdown-header { + display: block; + padding: 3px 20px; + font-size: 12px; + line-height: 1.846; + color: #bbbbbb; + white-space: nowrap; +} +.dropdown-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 990; +} +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + content: ""; + border-top: 0; + border-bottom: 4px dashed; + border-bottom: 4px solid \9; +} +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 2px; +} +@media (min-width: 768px) { + .navbar-right .dropdown-menu { + right: 0; + left: auto; + } + .navbar-right .dropdown-menu-left { + right: auto; + left: 0; + } +} +.btn-group, +.btn-group-vertical { + position: relative; + display: inline-block; + vertical-align: middle; +} +.btn-group > .btn, +.btn-group-vertical > .btn { + position: relative; + float: left; +} +.btn-group > .btn:hover, +.btn-group-vertical > .btn:hover, +.btn-group > .btn:focus, +.btn-group-vertical > .btn:focus, +.btn-group > .btn:active, +.btn-group-vertical > .btn:active, +.btn-group > .btn.active, +.btn-group-vertical > .btn.active { + z-index: 2; +} +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group { + margin-left: -1px; +} +.btn-toolbar { + margin-left: -5px; +} +.btn-toolbar .btn, +.btn-toolbar .btn-group, +.btn-toolbar .input-group { + float: left; +} +.btn-toolbar > .btn, +.btn-toolbar > .btn-group, +.btn-toolbar > .input-group { + margin-left: 5px; +} +.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { + border-radius: 0; +} +.btn-group > .btn:first-child { + margin-left: 0; +} +.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group > .btn:last-child:not(:first-child), +.btn-group > .dropdown-toggle:not(:first-child) { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group > .btn-group { + float: left; +} +.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} +.btn-group > .btn + .dropdown-toggle { + padding-right: 8px; + padding-left: 8px; +} +.btn-group > .btn-lg + .dropdown-toggle { + padding-right: 12px; + padding-left: 12px; +} +.btn-group.open .dropdown-toggle { + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn-group.open .dropdown-toggle.btn-link { + box-shadow: none; +} +.btn .caret { + margin-left: 0; +} +.btn-lg .caret { + border-width: 5px 5px 0; + border-bottom-width: 0; +} +.dropup .btn-lg .caret { + border-width: 0 5px 5px; +} +.btn-group-vertical > .btn, +.btn-group-vertical > .btn-group, +.btn-group-vertical > .btn-group > .btn { + display: block; + float: none; + width: 100%; + max-width: 100%; +} +.btn-group-vertical > .btn-group > .btn { + float: none; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: -1px; + margin-left: 0; +} +.btn-group-vertical > .btn:not(:first-child):not(:last-child) { + border-radius: 0; +} +.btn-group-vertical > .btn:first-child:not(:last-child) { + border-top-left-radius: 3px; + border-top-right-radius: 3px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn:last-child:not(:first-child) { + border-top-left-radius: 0; + border-top-right-radius: 0; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.btn-group-justified { + display: table; + width: 100%; + table-layout: fixed; + border-collapse: separate; +} +.btn-group-justified > .btn, +.btn-group-justified > .btn-group { + display: table-cell; + float: none; + width: 1%; +} +.btn-group-justified > .btn-group .btn { + width: 100%; +} +.btn-group-justified > .btn-group .dropdown-menu { + left: auto; +} +[data-toggle="buttons"] > .btn input[type="radio"], +[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], +[data-toggle="buttons"] > .btn input[type="checkbox"], +[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { + position: absolute; + clip: rect(0, 0, 0, 0); + pointer-events: none; +} +.input-group { + position: relative; + display: table; + border-collapse: separate; +} +.input-group[class*="col-"] { + float: none; + padding-right: 0; + padding-left: 0; +} +.input-group .form-control { + position: relative; + z-index: 2; + float: left; + width: 100%; + margin-bottom: 0; +} +.input-group .form-control:focus { + z-index: 3; +} +.input-group-lg > .form-control, +.input-group-lg > .input-group-addon, +.input-group-lg > .input-group-btn > .btn { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +select.input-group-lg > .form-control, +select.input-group-lg > .input-group-addon, +select.input-group-lg > .input-group-btn > .btn { + height: 45px; + line-height: 45px; +} +textarea.input-group-lg > .form-control, +textarea.input-group-lg > .input-group-addon, +textarea.input-group-lg > .input-group-btn > .btn, +select[multiple].input-group-lg > .form-control, +select[multiple].input-group-lg > .input-group-addon, +select[multiple].input-group-lg > .input-group-btn > .btn { + height: auto; +} +.input-group-sm > .form-control, +.input-group-sm > .input-group-addon, +.input-group-sm > .input-group-btn > .btn { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +select.input-group-sm > .form-control, +select.input-group-sm > .input-group-addon, +select.input-group-sm > .input-group-btn > .btn { + height: 30px; + line-height: 30px; +} +textarea.input-group-sm > .form-control, +textarea.input-group-sm > .input-group-addon, +textarea.input-group-sm > .input-group-btn > .btn, +select[multiple].input-group-sm > .form-control, +select[multiple].input-group-sm > .input-group-addon, +select[multiple].input-group-sm > .input-group-btn > .btn { + height: auto; +} +.input-group-addon, +.input-group-btn, +.input-group .form-control { + display: table-cell; +} +.input-group-addon:not(:first-child):not(:last-child), +.input-group-btn:not(:first-child):not(:last-child), +.input-group .form-control:not(:first-child):not(:last-child) { + border-radius: 0; +} +.input-group-addon, +.input-group-btn { + width: 1%; + white-space: nowrap; + vertical-align: middle; +} +.input-group-addon { + padding: 6px 16px; + font-size: 13px; + font-weight: 400; + line-height: 1; + color: #666666; + text-align: center; + background-color: transparent; + border: 1px solid transparent; + border-radius: 3px; +} +.input-group-addon.input-sm { + padding: 5px 10px; + font-size: 12px; + border-radius: 3px; +} +.input-group-addon.input-lg { + padding: 10px 16px; + font-size: 17px; + border-radius: 3px; +} +.input-group-addon input[type="radio"], +.input-group-addon input[type="checkbox"] { + margin-top: 0; +} +.input-group .form-control:first-child, +.input-group-addon:first-child, +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group > .btn, +.input-group-btn:first-child > .dropdown-toggle, +.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), +.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.input-group-addon:first-child { + border-right: 0; +} +.input-group .form-control:last-child, +.input-group-addon:last-child, +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group > .btn, +.input-group-btn:last-child > .dropdown-toggle, +.input-group-btn:first-child > .btn:not(:first-child), +.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.input-group-addon:last-child { + border-left: 0; +} +.input-group-btn { + position: relative; + font-size: 0; + white-space: nowrap; +} +.input-group-btn > .btn { + position: relative; +} +.input-group-btn > .btn + .btn { + margin-left: -1px; +} +.input-group-btn > .btn:hover, +.input-group-btn > .btn:focus, +.input-group-btn > .btn:active { + z-index: 2; +} +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group { + margin-right: -1px; +} +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group { + z-index: 2; + margin-left: -1px; +} +.nav { + padding-left: 0; + margin-bottom: 0; + list-style: none; +} +.nav > li { + position: relative; + display: block; +} +.nav > li > a { + position: relative; + display: block; + padding: 10px 15px; +} +.nav > li > a:hover, +.nav > li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.nav > li.disabled > a { + color: #bbbbbb; +} +.nav > li.disabled > a:hover, +.nav > li.disabled > a:focus { + color: #bbbbbb; + text-decoration: none; + cursor: not-allowed; + background-color: transparent; +} +.nav .open > a, +.nav .open > a:hover, +.nav .open > a:focus { + background-color: #eeeeee; + border-color: #2196f3; +} +.nav .nav-divider { + height: 1px; + margin: 10.5px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.nav > li > a > img { + max-width: none; +} +.nav-tabs { + border-bottom: 1px solid transparent; +} +.nav-tabs > li { + float: left; + margin-bottom: -1px; +} +.nav-tabs > li > a { + margin-right: 2px; + line-height: 1.846; + border: 1px solid transparent; + border-radius: 3px 3px 0 0; +} +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee transparent; +} +.nav-tabs > li.active > a, +.nav-tabs > li.active > a:hover, +.nav-tabs > li.active > a:focus { + color: #666666; + cursor: default; + background-color: transparent; + border: 1px solid transparent; + border-bottom-color: transparent; +} +.nav-tabs.nav-justified { + width: 100%; + border-bottom: 0; +} +.nav-tabs.nav-justified > li { + float: none; +} +.nav-tabs.nav-justified > li > a { + margin-bottom: 5px; + text-align: center; +} +.nav-tabs.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-tabs.nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs.nav-justified > li > a { + margin-right: 0; + border-radius: 3px; +} +.nav-tabs.nav-justified > .active > a, +.nav-tabs.nav-justified > .active > a:hover, +.nav-tabs.nav-justified > .active > a:focus { + border: 1px solid transparent; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li > a { + border-bottom: 1px solid transparent; + border-radius: 3px 3px 0 0; + } + .nav-tabs.nav-justified > .active > a, + .nav-tabs.nav-justified > .active > a:hover, + .nav-tabs.nav-justified > .active > a:focus { + border-bottom-color: #ffffff; + } +} +.nav-pills > li { + float: left; +} +.nav-pills > li > a { + border-radius: 3px; +} +.nav-pills > li + li { + margin-left: 2px; +} +.nav-pills > li.active > a, +.nav-pills > li.active > a:hover, +.nav-pills > li.active > a:focus { + color: #ffffff; + background-color: #2196f3; +} +.nav-stacked > li { + float: none; +} +.nav-stacked > li + li { + margin-top: 2px; + margin-left: 0; +} +.nav-justified { + width: 100%; +} +.nav-justified > li { + float: none; +} +.nav-justified > li > a { + margin-bottom: 5px; + text-align: center; +} +.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs-justified { + border-bottom: 0; +} +.nav-tabs-justified > li > a { + margin-right: 0; + border-radius: 3px; +} +.nav-tabs-justified > .active > a, +.nav-tabs-justified > .active > a:hover, +.nav-tabs-justified > .active > a:focus { + border: 1px solid transparent; +} +@media (min-width: 768px) { + .nav-tabs-justified > li > a { + border-bottom: 1px solid transparent; + border-radius: 3px 3px 0 0; + } + .nav-tabs-justified > .active > a, + .nav-tabs-justified > .active > a:hover, + .nav-tabs-justified > .active > a:focus { + border-bottom-color: #ffffff; + } +} +.tab-content > .tab-pane { + display: none; +} +.tab-content > .active { + display: block; +} +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.navbar { + position: relative; + min-height: 64px; + margin-bottom: 23px; + border: 1px solid transparent; +} +@media (min-width: 768px) { + .navbar { + border-radius: 3px; + } +} +@media (min-width: 768px) { + .navbar-header { + float: left; + } +} +.navbar-collapse { + padding-right: 15px; + padding-left: 15px; + overflow-x: visible; + border-top: 1px solid transparent; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); + -webkit-overflow-scrolling: touch; +} +.navbar-collapse.in { + overflow-y: auto; +} +@media (min-width: 768px) { + .navbar-collapse { + width: auto; + border-top: 0; + box-shadow: none; + } + .navbar-collapse.collapse { + display: block !important; + height: auto !important; + padding-bottom: 0; + overflow: visible !important; + } + .navbar-collapse.in { + overflow-y: visible; + } + .navbar-fixed-top .navbar-collapse, + .navbar-static-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + padding-right: 0; + padding-left: 0; + } +} +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; +} +.navbar-fixed-top .navbar-collapse, +.navbar-fixed-bottom .navbar-collapse { + max-height: 340px; +} +@media (max-device-width: 480px) and (orientation: landscape) { + .navbar-fixed-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + max-height: 200px; + } +} +@media (min-width: 768px) { + .navbar-fixed-top, + .navbar-fixed-bottom { + border-radius: 0; + } +} +.navbar-fixed-top { + top: 0; + border-width: 0 0 1px; +} +.navbar-fixed-bottom { + bottom: 0; + margin-bottom: 0; + border-width: 1px 0 0; +} +.container > .navbar-header, +.container-fluid > .navbar-header, +.container > .navbar-collapse, +.container-fluid > .navbar-collapse { + margin-right: -15px; + margin-left: -15px; +} +@media (min-width: 768px) { + .container > .navbar-header, + .container-fluid > .navbar-header, + .container > .navbar-collapse, + .container-fluid > .navbar-collapse { + margin-right: 0; + margin-left: 0; + } +} +.navbar-static-top { + z-index: 1000; + border-width: 0 0 1px; +} +@media (min-width: 768px) { + .navbar-static-top { + border-radius: 0; + } +} +.navbar-brand { + float: left; + height: 64px; + padding: 10.5px 15px; + font-size: 17px; + line-height: 35px; +} +.navbar-brand:hover, +.navbar-brand:focus { + text-decoration: none; +} +.navbar-brand > img { + display: block; +} +@media (min-width: 768px) { + .navbar > .container .navbar-brand, + .navbar > .container-fluid .navbar-brand { + margin-left: -15px; + } +} +.navbar-toggle { + position: relative; + float: right; + padding: 9px 10px; + margin-right: 15px; + margin-top: 15px; + margin-bottom: 15px; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 3px; +} +.navbar-toggle:focus { + outline: 0; +} +.navbar-toggle .icon-bar { + display: block; + width: 22px; + height: 2px; + border-radius: 1px; +} +.navbar-toggle .icon-bar + .icon-bar { + margin-top: 4px; +} +@media (min-width: 768px) { + .navbar-toggle { + display: none; + } +} +.navbar-nav { + margin: 10.25px -15px; +} +.navbar-nav > li > a { + padding-top: 10px; + padding-bottom: 10px; + line-height: 23px; +} +@media (max-width: 767px) { + .navbar-nav .open .dropdown-menu { + position: static; + float: none; + width: auto; + margin-top: 0; + background-color: transparent; + border: 0; + box-shadow: none; + } + .navbar-nav .open .dropdown-menu > li > a, + .navbar-nav .open .dropdown-menu .dropdown-header { + padding: 5px 15px 5px 25px; + } + .navbar-nav .open .dropdown-menu > li > a { + line-height: 23px; + } + .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-nav .open .dropdown-menu > li > a:focus { + background-image: none; + } +} +@media (min-width: 768px) { + .navbar-nav { + float: left; + margin: 0; + } + .navbar-nav > li { + float: left; + } + .navbar-nav > li > a { + padding-top: 20.5px; + padding-bottom: 20.5px; + } +} +.navbar-form { + padding: 10px 15px; + margin-right: -15px; + margin-left: -15px; + border-top: 1px solid transparent; + border-bottom: 1px solid transparent; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + margin-top: 13.5px; + margin-bottom: 13.5px; +} +@media (min-width: 768px) { + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .navbar-form .form-control-static { + display: inline-block; + } + .navbar-form .input-group { + display: inline-table; + vertical-align: middle; + } + .navbar-form .input-group .input-group-addon, + .navbar-form .input-group .input-group-btn, + .navbar-form .input-group .form-control { + width: auto; + } + .navbar-form .input-group > .form-control { + width: 100%; + } + .navbar-form .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio, + .navbar-form .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio label, + .navbar-form .checkbox label { + padding-left: 0; + } + .navbar-form .radio input[type="radio"], + .navbar-form .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .navbar-form .has-feedback .form-control-feedback { + top: 0; + } +} +@media (max-width: 767px) { + .navbar-form .form-group { + margin-bottom: 5px; + } + .navbar-form .form-group:last-child { + margin-bottom: 0; + } +} +@media (min-width: 768px) { + .navbar-form { + width: auto; + padding-top: 0; + padding-bottom: 0; + margin-right: 0; + margin-left: 0; + border: 0; + box-shadow: none; + } +} +.navbar-nav > li > .dropdown-menu { + margin-top: 0; + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { + margin-bottom: 0; + border-top-left-radius: 3px; + border-top-right-radius: 3px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.navbar-btn { + margin-top: 13.5px; + margin-bottom: 13.5px; +} +.navbar-btn.btn-sm { + margin-top: 17px; + margin-bottom: 17px; +} +.navbar-btn.btn-xs { + margin-top: 21px; + margin-bottom: 21px; +} +.navbar-text { + margin-top: 20.5px; + margin-bottom: 20.5px; +} +@media (min-width: 768px) { + .navbar-text { + float: left; + margin-right: 15px; + margin-left: 15px; + } +} +@media (min-width: 768px) { + .navbar-left { + float: left !important; + } + .navbar-right { + float: right !important; + margin-right: -15px; + } + .navbar-right ~ .navbar-right { + margin-right: 0; + } +} +.navbar-default { + background-color: #ffffff; + border-color: transparent; +} +.navbar-default .navbar-brand { + color: #666666; +} +.navbar-default .navbar-brand:hover, +.navbar-default .navbar-brand:focus { + color: #212121; + background-color: transparent; +} +.navbar-default .navbar-text { + color: #bbbbbb; +} +.navbar-default .navbar-nav > li > a { + color: #666666; +} +.navbar-default .navbar-nav > li > a:hover, +.navbar-default .navbar-nav > li > a:focus { + color: #212121; + background-color: transparent; +} +.navbar-default .navbar-nav > .active > a, +.navbar-default .navbar-nav > .active > a:hover, +.navbar-default .navbar-nav > .active > a:focus { + color: #212121; + background-color: #eeeeee; +} +.navbar-default .navbar-nav > .disabled > a, +.navbar-default .navbar-nav > .disabled > a:hover, +.navbar-default .navbar-nav > .disabled > a:focus { + color: #cccccc; + background-color: transparent; +} +.navbar-default .navbar-nav > .open > a, +.navbar-default .navbar-nav > .open > a:hover, +.navbar-default .navbar-nav > .open > a:focus { + color: #212121; + background-color: #eeeeee; +} +@media (max-width: 767px) { + .navbar-default .navbar-nav .open .dropdown-menu > li > a { + color: #666666; + } + .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { + color: #212121; + background-color: transparent; + } + .navbar-default .navbar-nav .open .dropdown-menu > .active > a, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #212121; + background-color: #eeeeee; + } + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #cccccc; + background-color: transparent; + } +} +.navbar-default .navbar-toggle { + border-color: transparent; +} +.navbar-default .navbar-toggle:hover, +.navbar-default .navbar-toggle:focus { + background-color: transparent; +} +.navbar-default .navbar-toggle .icon-bar { + background-color: rgba(0, 0, 0, 0.5); +} +.navbar-default .navbar-collapse, +.navbar-default .navbar-form { + border-color: transparent; +} +.navbar-default .navbar-link { + color: #666666; +} +.navbar-default .navbar-link:hover { + color: #212121; +} +.navbar-default .btn-link { + color: #666666; +} +.navbar-default .btn-link:hover, +.navbar-default .btn-link:focus { + color: #212121; +} +.navbar-default .btn-link[disabled]:hover, +fieldset[disabled] .navbar-default .btn-link:hover, +.navbar-default .btn-link[disabled]:focus, +fieldset[disabled] .navbar-default .btn-link:focus { + color: #cccccc; +} +.navbar-inverse { + background-color: #2196f3; + border-color: transparent; +} +.navbar-inverse .navbar-brand { + color: #b2dbfb; +} +.navbar-inverse .navbar-brand:hover, +.navbar-inverse .navbar-brand:focus { + color: #ffffff; + background-color: transparent; +} +.navbar-inverse .navbar-text { + color: #bbbbbb; +} +.navbar-inverse .navbar-nav > li > a { + color: #b2dbfb; +} +.navbar-inverse .navbar-nav > li > a:hover, +.navbar-inverse .navbar-nav > li > a:focus { + color: #ffffff; + background-color: transparent; +} +.navbar-inverse .navbar-nav > .active > a, +.navbar-inverse .navbar-nav > .active > a:hover, +.navbar-inverse .navbar-nav > .active > a:focus { + color: #ffffff; + background-color: #0c7cd5; +} +.navbar-inverse .navbar-nav > .disabled > a, +.navbar-inverse .navbar-nav > .disabled > a:hover, +.navbar-inverse .navbar-nav > .disabled > a:focus { + color: #444444; + background-color: transparent; +} +.navbar-inverse .navbar-nav > .open > a, +.navbar-inverse .navbar-nav > .open > a:hover, +.navbar-inverse .navbar-nav > .open > a:focus { + color: #ffffff; + background-color: #0c7cd5; +} +@media (max-width: 767px) { + .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { + border-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu .divider { + background-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { + color: #b2dbfb; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { + color: #ffffff; + background-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #ffffff; + background-color: #0c7cd5; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #444444; + background-color: transparent; + } +} +.navbar-inverse .navbar-toggle { + border-color: transparent; +} +.navbar-inverse .navbar-toggle:hover, +.navbar-inverse .navbar-toggle:focus { + background-color: transparent; +} +.navbar-inverse .navbar-toggle .icon-bar { + background-color: rgba(0, 0, 0, 0.5); +} +.navbar-inverse .navbar-collapse, +.navbar-inverse .navbar-form { + border-color: #0c84e4; +} +.navbar-inverse .navbar-link { + color: #b2dbfb; +} +.navbar-inverse .navbar-link:hover { + color: #ffffff; +} +.navbar-inverse .btn-link { + color: #b2dbfb; +} +.navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link:focus { + color: #ffffff; +} +.navbar-inverse .btn-link[disabled]:hover, +fieldset[disabled] .navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link[disabled]:focus, +fieldset[disabled] .navbar-inverse .btn-link:focus { + color: #444444; +} +.breadcrumb { + padding: 8px 15px; + margin-bottom: 23px; + list-style: none; + background-color: #f5f5f5; + border-radius: 3px; +} +.breadcrumb > li { + display: inline-block; +} +.breadcrumb > li + li:before { + padding: 0 5px; + color: #cccccc; + content: "/\00a0"; +} +.breadcrumb > .active { + color: #bbbbbb; +} +.pagination { + display: inline-block; + padding-left: 0; + margin: 23px 0; + border-radius: 3px; +} +.pagination > li { + display: inline; +} +.pagination > li > a, +.pagination > li > span { + position: relative; + float: left; + padding: 6px 16px; + margin-left: -1px; + line-height: 1.846; + color: #2196f3; + text-decoration: none; + background-color: #ffffff; + border: 1px solid #dddddd; +} +.pagination > li > a:hover, +.pagination > li > span:hover, +.pagination > li > a:focus, +.pagination > li > span:focus { + z-index: 2; + color: #0a6ebd; + background-color: #eeeeee; + border-color: #dddddd; +} +.pagination > li:first-child > a, +.pagination > li:first-child > span { + margin-left: 0; + border-top-left-radius: 3px; + border-bottom-left-radius: 3px; +} +.pagination > li:last-child > a, +.pagination > li:last-child > span { + border-top-right-radius: 3px; + border-bottom-right-radius: 3px; +} +.pagination > .active > a, +.pagination > .active > span, +.pagination > .active > a:hover, +.pagination > .active > span:hover, +.pagination > .active > a:focus, +.pagination > .active > span:focus { + z-index: 3; + color: #ffffff; + cursor: default; + background-color: #2196f3; + border-color: #2196f3; +} +.pagination > .disabled > span, +.pagination > .disabled > span:hover, +.pagination > .disabled > span:focus, +.pagination > .disabled > a, +.pagination > .disabled > a:hover, +.pagination > .disabled > a:focus { + color: #bbbbbb; + cursor: not-allowed; + background-color: #ffffff; + border-color: #dddddd; +} +.pagination-lg > li > a, +.pagination-lg > li > span { + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; +} +.pagination-lg > li:first-child > a, +.pagination-lg > li:first-child > span { + border-top-left-radius: 3px; + border-bottom-left-radius: 3px; +} +.pagination-lg > li:last-child > a, +.pagination-lg > li:last-child > span { + border-top-right-radius: 3px; + border-bottom-right-radius: 3px; +} +.pagination-sm > li > a, +.pagination-sm > li > span { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; +} +.pagination-sm > li:first-child > a, +.pagination-sm > li:first-child > span { + border-top-left-radius: 3px; + border-bottom-left-radius: 3px; +} +.pagination-sm > li:last-child > a, +.pagination-sm > li:last-child > span { + border-top-right-radius: 3px; + border-bottom-right-radius: 3px; +} +.pager { + padding-left: 0; + margin: 23px 0; + text-align: center; + list-style: none; +} +.pager li { + display: inline; +} +.pager li > a, +.pager li > span { + display: inline-block; + padding: 5px 14px; + background-color: #ffffff; + border: 1px solid #dddddd; + border-radius: 15px; +} +.pager li > a:hover, +.pager li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.pager .next > a, +.pager .next > span { + float: right; +} +.pager .previous > a, +.pager .previous > span { + float: left; +} +.pager .disabled > a, +.pager .disabled > a:hover, +.pager .disabled > a:focus, +.pager .disabled > span { + color: #bbbbbb; + cursor: not-allowed; + background-color: #ffffff; +} +.label { + display: inline; + padding: .2em .6em .3em; + font-size: 75%; + font-weight: 700; + line-height: 1; + color: #ffffff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: .25em; +} +a.label:hover, +a.label:focus { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} +.label:empty { + display: none; +} +.btn .label { + position: relative; + top: -1px; +} +.label-default { + background-color: #bbbbbb; +} +.label-default[href]:hover, +.label-default[href]:focus { + background-color: #a2a2a2; +} +.label-primary { + background-color: #2196f3; +} +.label-primary[href]:hover, +.label-primary[href]:focus { + background-color: #0c7cd5; +} +.label-success { + background-color: #4caf50; +} +.label-success[href]:hover, +.label-success[href]:focus { + background-color: #3d8b40; +} +.label-info { + background-color: #9c27b0; +} +.label-info[href]:hover, +.label-info[href]:focus { + background-color: #771e86; +} +.label-warning { + background-color: #ff9800; +} +.label-warning[href]:hover, +.label-warning[href]:focus { + background-color: #cc7a00; +} +.label-danger { + background-color: #e51c23; +} +.label-danger[href]:hover, +.label-danger[href]:focus { + background-color: #b9151b; +} +.badge { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: normal; + line-height: 1; + color: #ffffff; + text-align: center; + white-space: nowrap; + vertical-align: middle; + background-color: #bbbbbb; + border-radius: 10px; +} +.badge:empty { + display: none; +} +.btn .badge { + position: relative; + top: -1px; +} +.btn-xs .badge, +.btn-group-xs > .btn .badge { + top: 0; + padding: 1px 5px; +} +a.badge:hover, +a.badge:focus { + color: #ffffff; + text-decoration: none; + cursor: pointer; +} +.list-group-item.active > .badge, +.nav-pills > .active > a > .badge { + color: #2196f3; + background-color: #ffffff; +} +.list-group-item > .badge { + float: right; +} +.list-group-item > .badge + .badge { + margin-right: 5px; +} +.nav-pills > li > a > .badge { + margin-left: 3px; +} +.jumbotron { + padding-top: 30px; + padding-bottom: 30px; + margin-bottom: 30px; + color: inherit; + background-color: #f9f9f9; +} +.jumbotron h1, +.jumbotron .h1 { + color: #444444; +} +.jumbotron p { + margin-bottom: 15px; + font-size: 20px; + font-weight: 200; +} +.jumbotron > hr { + border-top-color: #e0e0e0; +} +.container .jumbotron, +.container-fluid .jumbotron { + padding-right: 15px; + padding-left: 15px; + border-radius: 3px; +} +.jumbotron .container { + max-width: 100%; +} +@media screen and (min-width: 768px) { + .jumbotron { + padding-top: 48px; + padding-bottom: 48px; + } + .container .jumbotron, + .container-fluid .jumbotron { + padding-right: 60px; + padding-left: 60px; + } + .jumbotron h1, + .jumbotron .h1 { + font-size: 59px; + } +} +.thumbnail { + display: block; + padding: 4px; + margin-bottom: 23px; + line-height: 1.846; + background-color: #ffffff; + border: 1px solid #dddddd; + border-radius: 3px; + transition: border 0.2s ease-in-out; +} +.thumbnail > img, +.thumbnail a > img { + margin-right: auto; + margin-left: auto; +} +a.thumbnail:hover, +a.thumbnail:focus, +a.thumbnail.active { + border-color: #2196f3; +} +.thumbnail .caption { + padding: 9px; + color: #666666; +} +.alert { + padding: 15px; + margin-bottom: 23px; + border: 1px solid transparent; + border-radius: 3px; +} +.alert h4 { + margin-top: 0; + color: inherit; +} +.alert .alert-link { + font-weight: bold; +} +.alert > p, +.alert > ul { + margin-bottom: 0; +} +.alert > p + p { + margin-top: 5px; +} +.alert-dismissable, +.alert-dismissible { + padding-right: 35px; +} +.alert-dismissable .close, +.alert-dismissible .close { + position: relative; + top: -2px; + right: -21px; + color: inherit; +} +.alert-success { + color: #4caf50; + background-color: #dff0d8; + border-color: #d6e9c6; +} +.alert-success hr { + border-top-color: #c9e2b3; +} +.alert-success .alert-link { + color: #3d8b40; +} +.alert-info { + color: #9c27b0; + background-color: #e1bee7; + border-color: #cba4dd; +} +.alert-info hr { + border-top-color: #c191d6; +} +.alert-info .alert-link { + color: #771e86; +} +.alert-warning { + color: #ff9800; + background-color: #ffe0b2; + border-color: #ffc599; +} +.alert-warning hr { + border-top-color: #ffb67f; +} +.alert-warning .alert-link { + color: #cc7a00; +} +.alert-danger { + color: #e51c23; + background-color: #f9bdbb; + border-color: #f7a4af; +} +.alert-danger hr { + border-top-color: #f58c9a; +} +.alert-danger .alert-link { + color: #b9151b; +} +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +.progress { + height: 23px; + margin-bottom: 23px; + overflow: hidden; + background-color: #f5f5f5; + border-radius: 3px; + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} +.progress-bar { + float: left; + width: 0%; + height: 100%; + font-size: 12px; + line-height: 23px; + color: #ffffff; + text-align: center; + background-color: #2196f3; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + transition: width 0.6s ease; +} +.progress-striped .progress-bar, +.progress-bar-striped { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-size: 40px 40px; +} +.progress.active .progress-bar, +.progress-bar.active { + -webkit-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} +.progress-bar-success { + background-color: #4caf50; +} +.progress-striped .progress-bar-success { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-info { + background-color: #9c27b0; +} +.progress-striped .progress-bar-info { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-warning { + background-color: #ff9800; +} +.progress-striped .progress-bar-warning { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-danger { + background-color: #e51c23; +} +.progress-striped .progress-bar-danger { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.media { + margin-top: 15px; +} +.media:first-child { + margin-top: 0; +} +.media, +.media-body { + overflow: hidden; + zoom: 1; +} +.media-body { + width: 10000px; +} +.media-object { + display: block; +} +.media-object.img-thumbnail { + max-width: none; +} +.media-right, +.media > .pull-right { + padding-left: 10px; +} +.media-left, +.media > .pull-left { + padding-right: 10px; +} +.media-left, +.media-right, +.media-body { + display: table-cell; + vertical-align: top; +} +.media-middle { + vertical-align: middle; +} +.media-bottom { + vertical-align: bottom; +} +.media-heading { + margin-top: 0; + margin-bottom: 5px; +} +.media-list { + padding-left: 0; + list-style: none; +} +.list-group { + padding-left: 0; + margin-bottom: 20px; +} +.list-group-item { + position: relative; + display: block; + padding: 10px 15px; + margin-bottom: -1px; + background-color: #ffffff; + border: 1px solid #dddddd; +} +.list-group-item:first-child { + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.list-group-item.disabled, +.list-group-item.disabled:hover, +.list-group-item.disabled:focus { + color: #bbbbbb; + cursor: not-allowed; + background-color: #eeeeee; +} +.list-group-item.disabled .list-group-item-heading, +.list-group-item.disabled:hover .list-group-item-heading, +.list-group-item.disabled:focus .list-group-item-heading { + color: inherit; +} +.list-group-item.disabled .list-group-item-text, +.list-group-item.disabled:hover .list-group-item-text, +.list-group-item.disabled:focus .list-group-item-text { + color: #bbbbbb; +} +.list-group-item.active, +.list-group-item.active:hover, +.list-group-item.active:focus { + z-index: 2; + color: #ffffff; + background-color: #2196f3; + border-color: #2196f3; +} +.list-group-item.active .list-group-item-heading, +.list-group-item.active:hover .list-group-item-heading, +.list-group-item.active:focus .list-group-item-heading, +.list-group-item.active .list-group-item-heading > small, +.list-group-item.active:hover .list-group-item-heading > small, +.list-group-item.active:focus .list-group-item-heading > small, +.list-group-item.active .list-group-item-heading > .small, +.list-group-item.active:hover .list-group-item-heading > .small, +.list-group-item.active:focus .list-group-item-heading > .small { + color: inherit; +} +.list-group-item.active .list-group-item-text, +.list-group-item.active:hover .list-group-item-text, +.list-group-item.active:focus .list-group-item-text { + color: #e3f2fd; +} +a.list-group-item, +button.list-group-item { + color: #555555; +} +a.list-group-item .list-group-item-heading, +button.list-group-item .list-group-item-heading { + color: #333333; +} +a.list-group-item:hover, +button.list-group-item:hover, +a.list-group-item:focus, +button.list-group-item:focus { + color: #555555; + text-decoration: none; + background-color: #f5f5f5; +} +button.list-group-item { + width: 100%; + text-align: left; +} +.list-group-item-success { + color: #4caf50; + background-color: #dff0d8; +} +a.list-group-item-success, +button.list-group-item-success { + color: #4caf50; +} +a.list-group-item-success .list-group-item-heading, +button.list-group-item-success .list-group-item-heading { + color: inherit; +} +a.list-group-item-success:hover, +button.list-group-item-success:hover, +a.list-group-item-success:focus, +button.list-group-item-success:focus { + color: #4caf50; + background-color: #d0e9c6; +} +a.list-group-item-success.active, +button.list-group-item-success.active, +a.list-group-item-success.active:hover, +button.list-group-item-success.active:hover, +a.list-group-item-success.active:focus, +button.list-group-item-success.active:focus { + color: #fff; + background-color: #4caf50; + border-color: #4caf50; +} +.list-group-item-info { + color: #9c27b0; + background-color: #e1bee7; +} +a.list-group-item-info, +button.list-group-item-info { + color: #9c27b0; +} +a.list-group-item-info .list-group-item-heading, +button.list-group-item-info .list-group-item-heading { + color: inherit; +} +a.list-group-item-info:hover, +button.list-group-item-info:hover, +a.list-group-item-info:focus, +button.list-group-item-info:focus { + color: #9c27b0; + background-color: #d8abe0; +} +a.list-group-item-info.active, +button.list-group-item-info.active, +a.list-group-item-info.active:hover, +button.list-group-item-info.active:hover, +a.list-group-item-info.active:focus, +button.list-group-item-info.active:focus { + color: #fff; + background-color: #9c27b0; + border-color: #9c27b0; +} +.list-group-item-warning { + color: #ff9800; + background-color: #ffe0b2; +} +a.list-group-item-warning, +button.list-group-item-warning { + color: #ff9800; +} +a.list-group-item-warning .list-group-item-heading, +button.list-group-item-warning .list-group-item-heading { + color: inherit; +} +a.list-group-item-warning:hover, +button.list-group-item-warning:hover, +a.list-group-item-warning:focus, +button.list-group-item-warning:focus { + color: #ff9800; + background-color: #ffd699; +} +a.list-group-item-warning.active, +button.list-group-item-warning.active, +a.list-group-item-warning.active:hover, +button.list-group-item-warning.active:hover, +a.list-group-item-warning.active:focus, +button.list-group-item-warning.active:focus { + color: #fff; + background-color: #ff9800; + border-color: #ff9800; +} +.list-group-item-danger { + color: #e51c23; + background-color: #f9bdbb; +} +a.list-group-item-danger, +button.list-group-item-danger { + color: #e51c23; +} +a.list-group-item-danger .list-group-item-heading, +button.list-group-item-danger .list-group-item-heading { + color: inherit; +} +a.list-group-item-danger:hover, +button.list-group-item-danger:hover, +a.list-group-item-danger:focus, +button.list-group-item-danger:focus { + color: #e51c23; + background-color: #f7a6a4; +} +a.list-group-item-danger.active, +button.list-group-item-danger.active, +a.list-group-item-danger.active:hover, +button.list-group-item-danger.active:hover, +a.list-group-item-danger.active:focus, +button.list-group-item-danger.active:focus { + color: #fff; + background-color: #e51c23; + border-color: #e51c23; +} +.list-group-item-heading { + margin-top: 0; + margin-bottom: 5px; +} +.list-group-item-text { + margin-bottom: 0; + line-height: 1.3; +} +.panel { + margin-bottom: 23px; + background-color: #ffffff; + border: 1px solid transparent; + border-radius: 3px; + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); +} +.panel-body { + padding: 15px; +} +.panel-heading { + padding: 10px 15px; + border-bottom: 1px solid transparent; + border-top-left-radius: 2px; + border-top-right-radius: 2px; +} +.panel-heading > .dropdown .dropdown-toggle { + color: inherit; +} +.panel-title { + margin-top: 0; + margin-bottom: 0; + font-size: 15px; + color: inherit; +} +.panel-title > a, +.panel-title > small, +.panel-title > .small, +.panel-title > small > a, +.panel-title > .small > a { + color: inherit; +} +.panel-footer { + padding: 10px 15px; + background-color: #f5f5f5; + border-top: 1px solid #dddddd; + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +.panel > .list-group, +.panel > .panel-collapse > .list-group { + margin-bottom: 0; +} +.panel > .list-group .list-group-item, +.panel > .panel-collapse > .list-group .list-group-item { + border-width: 1px 0; + border-radius: 0; +} +.panel > .list-group:first-child .list-group-item:first-child, +.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { + border-top: 0; + border-top-left-radius: 2px; + border-top-right-radius: 2px; +} +.panel > .list-group:last-child .list-group-item:last-child, +.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { + border-bottom: 0; + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { + border-top-left-radius: 0; + border-top-right-radius: 0; +} +.panel-heading + .list-group .list-group-item:first-child { + border-top-width: 0; +} +.list-group + .panel-footer { + border-top-width: 0; +} +.panel > .table, +.panel > .table-responsive > .table, +.panel > .panel-collapse > .table { + margin-bottom: 0; +} +.panel > .table caption, +.panel > .table-responsive > .table caption, +.panel > .panel-collapse > .table caption { + padding-right: 15px; + padding-left: 15px; +} +.panel > .table:first-child, +.panel > .table-responsive:first-child > .table:first-child { + border-top-left-radius: 2px; + border-top-right-radius: 2px; +} +.panel > .table:first-child > thead:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { + border-top-left-radius: 2px; + border-top-right-radius: 2px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { + border-top-left-radius: 2px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { + border-top-right-radius: 2px; +} +.panel > .table:last-child, +.panel > .table-responsive:last-child > .table:last-child { + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { + border-bottom-left-radius: 2px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { + border-bottom-right-radius: 2px; +} +.panel > .panel-body + .table, +.panel > .panel-body + .table-responsive, +.panel > .table + .panel-body, +.panel > .table-responsive + .panel-body { + border-top: 1px solid #dddddd; +} +.panel > .table > tbody:first-child > tr:first-child th, +.panel > .table > tbody:first-child > tr:first-child td { + border-top: 0; +} +.panel > .table-bordered, +.panel > .table-responsive > .table-bordered { + border: 0; +} +.panel > .table-bordered > thead > tr > th:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:first-child, +.panel > .table-bordered > tbody > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, +.panel > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-bordered > thead > tr > td:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:first-child, +.panel > .table-bordered > tbody > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, +.panel > .table-bordered > tfoot > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; +} +.panel > .table-bordered > thead > tr > th:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:last-child, +.panel > .table-bordered > tbody > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, +.panel > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-bordered > thead > tr > td:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, +.panel > .table-bordered > tbody > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, +.panel > .table-bordered > tfoot > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; +} +.panel > .table-bordered > thead > tr:first-child > td, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, +.panel > .table-bordered > tbody > tr:first-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, +.panel > .table-bordered > thead > tr:first-child > th, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, +.panel > .table-bordered > tbody > tr:first-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { + border-bottom: 0; +} +.panel > .table-bordered > tbody > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, +.panel > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-bordered > tbody > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, +.panel > .table-bordered > tfoot > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { + border-bottom: 0; +} +.panel > .table-responsive { + margin-bottom: 0; + border: 0; +} +.panel-group { + margin-bottom: 23px; +} +.panel-group .panel { + margin-bottom: 0; + border-radius: 3px; +} +.panel-group .panel + .panel { + margin-top: 5px; +} +.panel-group .panel-heading { + border-bottom: 0; +} +.panel-group .panel-heading + .panel-collapse > .panel-body, +.panel-group .panel-heading + .panel-collapse > .list-group { + border-top: 1px solid #dddddd; +} +.panel-group .panel-footer { + border-top: 0; +} +.panel-group .panel-footer + .panel-collapse .panel-body { + border-bottom: 1px solid #dddddd; +} +.panel-default { + border-color: #dddddd; +} +.panel-default > .panel-heading { + color: #212121; + background-color: #f5f5f5; + border-color: #dddddd; +} +.panel-default > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #dddddd; +} +.panel-default > .panel-heading .badge { + color: #f5f5f5; + background-color: #212121; +} +.panel-default > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #dddddd; +} +.panel-primary { + border-color: #2196f3; +} +.panel-primary > .panel-heading { + color: #ffffff; + background-color: #2196f3; + border-color: #2196f3; +} +.panel-primary > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #2196f3; +} +.panel-primary > .panel-heading .badge { + color: #2196f3; + background-color: #ffffff; +} +.panel-primary > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #2196f3; +} +.panel-success { + border-color: #d6e9c6; +} +.panel-success > .panel-heading { + color: #ffffff; + background-color: #4caf50; + border-color: #d6e9c6; +} +.panel-success > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #d6e9c6; +} +.panel-success > .panel-heading .badge { + color: #4caf50; + background-color: #ffffff; +} +.panel-success > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #d6e9c6; +} +.panel-info { + border-color: #cba4dd; +} +.panel-info > .panel-heading { + color: #ffffff; + background-color: #9c27b0; + border-color: #cba4dd; +} +.panel-info > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #cba4dd; +} +.panel-info > .panel-heading .badge { + color: #9c27b0; + background-color: #ffffff; +} +.panel-info > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #cba4dd; +} +.panel-warning { + border-color: #ffc599; +} +.panel-warning > .panel-heading { + color: #ffffff; + background-color: #ff9800; + border-color: #ffc599; +} +.panel-warning > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ffc599; +} +.panel-warning > .panel-heading .badge { + color: #ff9800; + background-color: #ffffff; +} +.panel-warning > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ffc599; +} +.panel-danger { + border-color: #f7a4af; +} +.panel-danger > .panel-heading { + color: #ffffff; + background-color: #e51c23; + border-color: #f7a4af; +} +.panel-danger > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #f7a4af; +} +.panel-danger > .panel-heading .badge { + color: #e51c23; + background-color: #ffffff; +} +.panel-danger > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #f7a4af; +} +.embed-responsive { + position: relative; + display: block; + height: 0; + padding: 0; + overflow: hidden; +} +.embed-responsive .embed-responsive-item, +.embed-responsive iframe, +.embed-responsive embed, +.embed-responsive object, +.embed-responsive video { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 100%; + height: 100%; + border: 0; +} +.embed-responsive-16by9 { + padding-bottom: 56.25%; +} +.embed-responsive-4by3 { + padding-bottom: 75%; +} +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f9f9f9; + border: 1px solid transparent; + border-radius: 3px; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} +.well-lg { + padding: 24px; + border-radius: 3px; +} +.well-sm { + padding: 9px; + border-radius: 3px; +} +.close { + float: right; + font-size: 19.5px; + font-weight: normal; + line-height: 1; + color: #000000; + text-shadow: none; + filter: alpha(opacity=20); + opacity: 0.2; +} +.close:hover, +.close:focus { + color: #000000; + text-decoration: none; + cursor: pointer; + filter: alpha(opacity=50); + opacity: 0.5; +} +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; + appearance: none; +} +.modal-open { + overflow: hidden; +} +.modal { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1050; + display: none; + overflow: hidden; + -webkit-overflow-scrolling: touch; + outline: 0; +} +.modal.fade .modal-dialog { + -webkit-transform: translate(0, -25%); + transform: translate(0, -25%); + transition: -webkit-transform 0.3s ease-out; + transition: transform 0.3s ease-out; +} +.modal.in .modal-dialog { + -webkit-transform: translate(0, 0); + transform: translate(0, 0); +} +.modal-open .modal { + overflow-x: hidden; + overflow-y: auto; +} +.modal-dialog { + position: relative; + width: auto; + margin: 10px; +} +.modal-content { + position: relative; + background-color: #ffffff; + background-clip: padding-box; + border: 1px solid #999999; + border: 1px solid transparent; + border-radius: 3px; + box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + outline: 0; +} +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000000; +} +.modal-backdrop.fade { + filter: alpha(opacity=0); + opacity: 0; +} +.modal-backdrop.in { + filter: alpha(opacity=50); + opacity: 0.5; +} +.modal-header { + padding: 15px; + border-bottom: 1px solid transparent; +} +.modal-header .close { + margin-top: -2px; +} +.modal-title { + margin: 0; + line-height: 1.846; +} +.modal-body { + position: relative; + padding: 15px; +} +.modal-footer { + padding: 15px; + text-align: right; + border-top: 1px solid transparent; +} +.modal-footer .btn + .btn { + margin-bottom: 0; + margin-left: 5px; +} +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} +.modal-footer .btn-block + .btn-block { + margin-left: 0; +} +.modal-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} +@media (min-width: 768px) { + .modal-dialog { + width: 600px; + margin: 30px auto; + } + .modal-content { + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + } + .modal-sm { + width: 300px; + } +} +@media (min-width: 992px) { + .modal-lg { + width: 900px; + } +} +.tooltip { + position: absolute; + z-index: 1070; + display: block; + font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: 400; + line-height: 1.846; + line-break: auto; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + white-space: normal; + font-size: 12px; + filter: alpha(opacity=0); + opacity: 0; +} +.tooltip.in { + filter: alpha(opacity=90); + opacity: 0.9; +} +.tooltip.top { + padding: 5px 0; + margin-top: -3px; +} +.tooltip.right { + padding: 0 5px; + margin-left: 3px; +} +.tooltip.bottom { + padding: 5px 0; + margin-top: 3px; +} +.tooltip.left { + padding: 0 5px; + margin-left: -3px; +} +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-width: 5px 5px 0; + border-top-color: #727272; +} +.tooltip.top-left .tooltip-arrow { + right: 5px; + bottom: 0; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #727272; +} +.tooltip.top-right .tooltip-arrow { + bottom: 0; + left: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #727272; +} +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-width: 5px 5px 5px 0; + border-right-color: #727272; +} +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-width: 5px 0 5px 5px; + border-left-color: #727272; +} +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-width: 0 5px 5px; + border-bottom-color: #727272; +} +.tooltip.bottom-left .tooltip-arrow { + top: 0; + right: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #727272; +} +.tooltip.bottom-right .tooltip-arrow { + top: 0; + left: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #727272; +} +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #ffffff; + text-align: center; + background-color: #727272; + border-radius: 3px; +} +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1060; + display: none; + max-width: 276px; + padding: 1px; + font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: 400; + line-height: 1.846; + line-break: auto; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + white-space: normal; + font-size: 13px; + background-color: #ffffff; + background-clip: padding-box; + border: 1px solid transparent; + border-radius: 3px; + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); +} +.popover.top { + margin-top: -10px; +} +.popover.right { + margin-left: 10px; +} +.popover.bottom { + margin-top: 10px; +} +.popover.left { + margin-left: -10px; +} +.popover > .arrow { + border-width: 11px; +} +.popover > .arrow, +.popover > .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.popover > .arrow:after { + content: ""; + border-width: 10px; +} +.popover.top > .arrow { + bottom: -11px; + left: 50%; + margin-left: -11px; + border-top-color: rgba(0, 0, 0, 0); + border-top-color: rgba(0, 0, 0, 0.075); + border-bottom-width: 0; +} +.popover.top > .arrow:after { + bottom: 1px; + margin-left: -10px; + content: " "; + border-top-color: #ffffff; + border-bottom-width: 0; +} +.popover.right > .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-right-color: rgba(0, 0, 0, 0); + border-right-color: rgba(0, 0, 0, 0.075); + border-left-width: 0; +} +.popover.right > .arrow:after { + bottom: -10px; + left: 1px; + content: " "; + border-right-color: #ffffff; + border-left-width: 0; +} +.popover.bottom > .arrow { + top: -11px; + left: 50%; + margin-left: -11px; + border-top-width: 0; + border-bottom-color: rgba(0, 0, 0, 0); + border-bottom-color: rgba(0, 0, 0, 0.075); +} +.popover.bottom > .arrow:after { + top: 1px; + margin-left: -10px; + content: " "; + border-top-width: 0; + border-bottom-color: #ffffff; +} +.popover.left > .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-right-width: 0; + border-left-color: rgba(0, 0, 0, 0); + border-left-color: rgba(0, 0, 0, 0.075); +} +.popover.left > .arrow:after { + right: 1px; + bottom: -10px; + content: " "; + border-right-width: 0; + border-left-color: #ffffff; +} +.popover-title { + padding: 8px 14px; + margin: 0; + font-size: 13px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + border-radius: 2px 2px 0 0; +} +.popover-content { + padding: 9px 14px; +} +.carousel { + position: relative; +} +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} +.carousel-inner > .item { + position: relative; + display: none; + transition: 0.6s ease-in-out left; +} +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + line-height: 1; +} +@media all and (transform-3d), (-webkit-transform-3d) { + .carousel-inner > .item { + transition: -webkit-transform 0.6s ease-in-out; + transition: transform 0.6s ease-in-out; + -webkit-backface-visibility: hidden; + backface-visibility: hidden; + -webkit-perspective: 1000px; + perspective: 1000px; + } + .carousel-inner > .item.next, + .carousel-inner > .item.active.right { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + left: 0; + } + .carousel-inner > .item.prev, + .carousel-inner > .item.active.left { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + left: 0; + } + .carousel-inner > .item.next.left, + .carousel-inner > .item.prev.right, + .carousel-inner > .item.active { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + left: 0; + } +} +.carousel-inner > .active, +.carousel-inner > .next, +.carousel-inner > .prev { + display: block; +} +.carousel-inner > .active { + left: 0; +} +.carousel-inner > .next, +.carousel-inner > .prev { + position: absolute; + top: 0; + width: 100%; +} +.carousel-inner > .next { + left: 100%; +} +.carousel-inner > .prev { + left: -100%; +} +.carousel-inner > .next.left, +.carousel-inner > .prev.right { + left: 0; +} +.carousel-inner > .active.left { + left: -100%; +} +.carousel-inner > .active.right { + left: 100%; +} +.carousel-control { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 15%; + font-size: 20px; + color: #ffffff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); + background-color: rgba(0, 0, 0, 0); + filter: alpha(opacity=50); + opacity: 0.5; +} +.carousel-control.left { + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); + background-repeat: repeat-x; +} +.carousel-control.right { + right: 0; + left: auto; + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); + background-repeat: repeat-x; +} +.carousel-control:hover, +.carousel-control:focus { + color: #ffffff; + text-decoration: none; + outline: 0; + filter: alpha(opacity=90); + opacity: 0.9; +} +.carousel-control .icon-prev, +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-left, +.carousel-control .glyphicon-chevron-right { + position: absolute; + top: 50%; + z-index: 5; + display: inline-block; + margin-top: -10px; +} +.carousel-control .icon-prev, +.carousel-control .glyphicon-chevron-left { + left: 50%; + margin-left: -10px; +} +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-right { + right: 50%; + margin-right: -10px; +} +.carousel-control .icon-prev, +.carousel-control .icon-next { + width: 20px; + height: 20px; + font-family: serif; + line-height: 1; +} +.carousel-control .icon-prev:before { + content: "\2039"; +} +.carousel-control .icon-next:before { + content: "\203a"; +} +.carousel-indicators { + position: absolute; + bottom: 10px; + left: 50%; + z-index: 15; + width: 60%; + padding-left: 0; + margin-left: -30%; + text-align: center; + list-style: none; +} +.carousel-indicators li { + display: inline-block; + width: 10px; + height: 10px; + margin: 1px; + text-indent: -999px; + cursor: pointer; + background-color: #000 \9; + background-color: rgba(0, 0, 0, 0); + border: 1px solid #ffffff; + border-radius: 10px; +} +.carousel-indicators .active { + width: 12px; + height: 12px; + margin: 0; + background-color: #ffffff; +} +.carousel-caption { + position: absolute; + right: 15%; + bottom: 20px; + left: 15%; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #ffffff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); +} +.carousel-caption .btn { + text-shadow: none; +} +@media screen and (min-width: 768px) { + .carousel-control .glyphicon-chevron-left, + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-prev, + .carousel-control .icon-next { + width: 30px; + height: 30px; + margin-top: -10px; + font-size: 30px; + } + .carousel-control .glyphicon-chevron-left, + .carousel-control .icon-prev { + margin-left: -10px; + } + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-next { + margin-right: -10px; + } + .carousel-caption { + right: 20%; + left: 20%; + padding-bottom: 30px; + } + .carousel-indicators { + bottom: 20px; + } +} +.clearfix:before, +.clearfix:after, +.dl-horizontal dd:before, +.dl-horizontal dd:after, +.container:before, +.container:after, +.container-fluid:before, +.container-fluid:after, +.row:before, +.row:after, +.form-horizontal .form-group:before, +.form-horizontal .form-group:after, +.btn-toolbar:before, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:before, +.btn-group-vertical > .btn-group:after, +.nav:before, +.nav:after, +.navbar:before, +.navbar:after, +.navbar-header:before, +.navbar-header:after, +.navbar-collapse:before, +.navbar-collapse:after, +.pager:before, +.pager:after, +.panel-body:before, +.panel-body:after, +.modal-header:before, +.modal-header:after, +.modal-footer:before, +.modal-footer:after { + display: table; + content: " "; +} +.clearfix:after, +.dl-horizontal dd:after, +.container:after, +.container-fluid:after, +.row:after, +.form-horizontal .form-group:after, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:after, +.nav:after, +.navbar:after, +.navbar-header:after, +.navbar-collapse:after, +.pager:after, +.panel-body:after, +.modal-header:after, +.modal-footer:after { + clear: both; +} +.center-block { + display: block; + margin-right: auto; + margin-left: auto; +} +.pull-right { + float: right !important; +} +.pull-left { + float: left !important; +} +.hide { + display: none !important; +} +.show { + display: block !important; +} +.invisible { + visibility: hidden; +} +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.hidden { + display: none !important; +} +.affix { + position: fixed; +} +@-ms-viewport { + width: device-width; +} +.visible-xs, +.visible-sm, +.visible-md, +.visible-lg { + display: none !important; +} +.visible-xs-block, +.visible-xs-inline, +.visible-xs-inline-block, +.visible-sm-block, +.visible-sm-inline, +.visible-sm-inline-block, +.visible-md-block, +.visible-md-inline, +.visible-md-inline-block, +.visible-lg-block, +.visible-lg-inline, +.visible-lg-inline-block { + display: none !important; +} +@media (max-width: 767px) { + .visible-xs { + display: block !important; + } + table.visible-xs { + display: table !important; + } + tr.visible-xs { + display: table-row !important; + } + th.visible-xs, + td.visible-xs { + display: table-cell !important; + } +} +@media (max-width: 767px) { + .visible-xs-block { + display: block !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline { + display: inline !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline-block { + display: inline-block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm { + display: block !important; + } + table.visible-sm { + display: table !important; + } + tr.visible-sm { + display: table-row !important; + } + th.visible-sm, + td.visible-sm { + display: table-cell !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-block { + display: block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline { + display: inline !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline-block { + display: inline-block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md { + display: block !important; + } + table.visible-md { + display: table !important; + } + tr.visible-md { + display: table-row !important; + } + th.visible-md, + td.visible-md { + display: table-cell !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-block { + display: block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline { + display: inline !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline-block { + display: inline-block !important; + } +} +@media (min-width: 1200px) { + .visible-lg { + display: block !important; + } + table.visible-lg { + display: table !important; + } + tr.visible-lg { + display: table-row !important; + } + th.visible-lg, + td.visible-lg { + display: table-cell !important; + } +} +@media (min-width: 1200px) { + .visible-lg-block { + display: block !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline { + display: inline !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline-block { + display: inline-block !important; + } +} +@media (max-width: 767px) { + .hidden-xs { + display: none !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .hidden-sm { + display: none !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .hidden-md { + display: none !important; + } +} +@media (min-width: 1200px) { + .hidden-lg { + display: none !important; + } +} +.visible-print { + display: none !important; +} +@media print { + .visible-print { + display: block !important; + } + table.visible-print { + display: table !important; + } + tr.visible-print { + display: table-row !important; + } + th.visible-print, + td.visible-print { + display: table-cell !important; + } +} +.visible-print-block { + display: none !important; +} +@media print { + .visible-print-block { + display: block !important; + } +} +.visible-print-inline { + display: none !important; +} +@media print { + .visible-print-inline { + display: inline !important; + } +} +.visible-print-inline-block { + display: none !important; +} +@media print { + .visible-print-inline-block { + display: inline-block !important; + } +} +@media print { + .hidden-print { + display: none !important; + } +} +.navbar { + border: none; + box-shadow: 0 1px 2px rgba(0, 0, 0, 0.3); +} +.navbar-brand { + font-size: 24px; +} +.navbar-inverse .navbar-form input[type=text], +.navbar-inverse .navbar-form input[type=password] { + color: #fff; + box-shadow: inset 0 -1px 0 #b2dbfb; +} +.navbar-inverse .navbar-form input[type=text]::-moz-placeholder, +.navbar-inverse .navbar-form input[type=password]::-moz-placeholder { + color: #b2dbfb; + opacity: 1; +} +.navbar-inverse .navbar-form input[type=text]:-ms-input-placeholder, +.navbar-inverse .navbar-form input[type=password]:-ms-input-placeholder { + color: #b2dbfb; +} +.navbar-inverse .navbar-form input[type=text]::-webkit-input-placeholder, +.navbar-inverse .navbar-form input[type=password]::-webkit-input-placeholder { + color: #b2dbfb; +} +.navbar-inverse .navbar-form input[type=text]:focus, +.navbar-inverse .navbar-form input[type=password]:focus { + box-shadow: inset 0 -2px 0 #ffffff; +} +.btn-default { + background-size: 200% 200%; + background-position: 50%; +} +.btn-default:focus { + background-color: #ffffff; +} +.btn-default:hover, +.btn-default:active:hover { + background-color: #f0f0f0; +} +.btn-default:active { + background-color: #e0e0e0; + background-image: radial-gradient(circle, #e0e0e0 10%, #ffffff 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-primary { + background-size: 200% 200%; + background-position: 50%; +} +.btn-primary:focus { + background-color: #2196f3; +} +.btn-primary:hover, +.btn-primary:active:hover { + background-color: #0d87e9; +} +.btn-primary:active { + background-color: #0b76cc; + background-image: radial-gradient(circle, #0b76cc 10%, #2196f3 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-success { + background-size: 200% 200%; + background-position: 50%; +} +.btn-success:focus { + background-color: #4caf50; +} +.btn-success:hover, +.btn-success:active:hover { + background-color: #439a46; +} +.btn-success:active { + background-color: #39843c; + background-image: radial-gradient(circle, #39843c 10%, #4caf50 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-info { + background-size: 200% 200%; + background-position: 50%; +} +.btn-info:focus { + background-color: #9c27b0; +} +.btn-info:hover, +.btn-info:active:hover { + background-color: #862197; +} +.btn-info:active { + background-color: #701c7e; + background-image: radial-gradient(circle, #701c7e 10%, #9c27b0 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-warning { + background-size: 200% 200%; + background-position: 50%; +} +.btn-warning:focus { + background-color: #ff9800; +} +.btn-warning:hover, +.btn-warning:active:hover { + background-color: #e08600; +} +.btn-warning:active { + background-color: #c27400; + background-image: radial-gradient(circle, #c27400 10%, #ff9800 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-danger { + background-size: 200% 200%; + background-position: 50%; +} +.btn-danger:focus { + background-color: #e51c23; +} +.btn-danger:hover, +.btn-danger:active:hover { + background-color: #cb171e; +} +.btn-danger:active { + background-color: #b0141a; + background-image: radial-gradient(circle, #b0141a 10%, #e51c23 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn-link { + background-size: 200% 200%; + background-position: 50%; +} +.btn-link:focus { + background-color: #ffffff; +} +.btn-link:hover, +.btn-link:active:hover { + background-color: #f0f0f0; +} +.btn-link:active { + background-color: #e0e0e0; + background-image: radial-gradient(circle, #e0e0e0 10%, #ffffff 11%); + background-repeat: no-repeat; + background-size: 1000% 1000%; + box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.4); +} +.btn { + text-transform: uppercase; + border: none; + box-shadow: 1px 1px 4px rgba(0, 0, 0, 0.4); + transition: all 0.4s; +} +.btn-link { + border-radius: 3px; + box-shadow: none; + color: #444444; +} +.btn-link:hover, +.btn-link:focus { + box-shadow: none; + color: #444444; + text-decoration: none; +} +.btn-default.disabled { + background-color: rgba(0, 0, 0, 0.1); + color: rgba(0, 0, 0, 0.4); + opacity: 1; +} +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group { + margin-left: 0; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: 0; +} +body { + -webkit-font-smoothing: antialiased; + letter-spacing: .1px; +} +p { + margin: 0 0 1em; +} +input, +button { + -webkit-font-smoothing: antialiased; + letter-spacing: .1px; +} +a { + transition: all 0.2s; +} +.table-hover > tbody > tr, +.table-hover > tbody > tr > th, +.table-hover > tbody > tr > td { + transition: all 0.2s; +} +label { + font-weight: normal; +} +textarea, +textarea.form-control, +input.form-control, +input[type=text], +input[type=password], +input[type=email], +input[type=number], +[type=text].form-control, +[type=password].form-control, +[type=email].form-control, +[type=tel].form-control, +[contenteditable].form-control { + padding: 0; + border: none; + border-radius: 0; + -webkit-appearance: none; + box-shadow: inset 0 -1px 0 #dddddd; + font-size: 16px; +} +textarea:focus, +textarea.form-control:focus, +input.form-control:focus, +input[type=text]:focus, +input[type=password]:focus, +input[type=email]:focus, +input[type=number]:focus, +[type=text].form-control:focus, +[type=password].form-control:focus, +[type=email].form-control:focus, +[type=tel].form-control:focus, +[contenteditable].form-control:focus { + box-shadow: inset 0 -2px 0 #2196f3; +} +textarea[disabled], +textarea.form-control[disabled], +input.form-control[disabled], +input[type=text][disabled], +input[type=password][disabled], +input[type=email][disabled], +input[type=number][disabled], +[type=text].form-control[disabled], +[type=password].form-control[disabled], +[type=email].form-control[disabled], +[type=tel].form-control[disabled], +[contenteditable].form-control[disabled], +textarea[readonly], +textarea.form-control[readonly], +input.form-control[readonly], +input[type=text][readonly], +input[type=password][readonly], +input[type=email][readonly], +input[type=number][readonly], +[type=text].form-control[readonly], +[type=password].form-control[readonly], +[type=email].form-control[readonly], +[type=tel].form-control[readonly], +[contenteditable].form-control[readonly] { + box-shadow: none; + border-bottom: 1px dotted #ddd; +} +textarea.input-sm, +textarea.form-control.input-sm, +input.form-control.input-sm, +input[type=text].input-sm, +input[type=password].input-sm, +input[type=email].input-sm, +input[type=number].input-sm, +[type=text].form-control.input-sm, +[type=password].form-control.input-sm, +[type=email].form-control.input-sm, +[type=tel].form-control.input-sm, +[contenteditable].form-control.input-sm { + font-size: 12px; +} +textarea.input-lg, +textarea.form-control.input-lg, +input.form-control.input-lg, +input[type=text].input-lg, +input[type=password].input-lg, +input[type=email].input-lg, +input[type=number].input-lg, +[type=text].form-control.input-lg, +[type=password].form-control.input-lg, +[type=email].form-control.input-lg, +[type=tel].form-control.input-lg, +[contenteditable].form-control.input-lg { + font-size: 17px; +} +select, +select.form-control { + border: 0; + border-radius: 0; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; + padding-left: 0; + padding-right: 0\9; + background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAMAAACelLz8AAAAJ1BMVEVmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmaP/QSjAAAADHRSTlMAAgMJC0uWpKa6wMxMdjkoAAAANUlEQVR4AeXJyQEAERAAsNl7Hf3X6xt0QL6JpZWq30pdvdadme+0PMdzvHm8YThHcT1H7K0BtOMDniZhWOgAAAAASUVORK5CYII=); + background-size: 13px; + background-repeat: no-repeat; + background-position: right center; + box-shadow: inset 0 -1px 0 #dddddd; + font-size: 16px; + line-height: 1.5; +} +select::-ms-expand, +select.form-control::-ms-expand { + display: none; +} +select.input-sm, +select.form-control.input-sm { + font-size: 12px; +} +select.input-lg, +select.form-control.input-lg { + font-size: 17px; +} +select:focus, +select.form-control:focus { + box-shadow: inset 0 -2px 0 #2196f3; + background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAMAAACelLz8AAAAJ1BMVEUhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISF8S9ewAAAADHRSTlMAAgMJC0uWpKa6wMxMdjkoAAAANUlEQVR4AeXJyQEAERAAsNl7Hf3X6xt0QL6JpZWq30pdvdadme+0PMdzvHm8YThHcT1H7K0BtOMDniZhWOgAAAAASUVORK5CYII=); +} +select[multiple], +select.form-control[multiple] { + background: none; +} +.radio label, +.radio-inline label, +.checkbox label, +.checkbox-inline label { + padding-left: 25px; +} +.radio input[type="radio"], +.radio-inline input[type="radio"], +.checkbox input[type="radio"], +.checkbox-inline input[type="radio"], +.radio input[type="checkbox"], +.radio-inline input[type="checkbox"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + margin-left: -25px; +} +input[type="radio"], +.radio input[type="radio"], +.radio-inline input[type="radio"] { + position: relative; + margin-top: 6px; + margin-right: 4px; + vertical-align: top; + border: none; + background-color: transparent; + -webkit-appearance: none; + appearance: none; + cursor: pointer; +} +input[type="radio"]:focus, +.radio input[type="radio"]:focus, +.radio-inline input[type="radio"]:focus { + outline: none; +} +input[type="radio"]:before, +.radio input[type="radio"]:before, +.radio-inline input[type="radio"]:before, +input[type="radio"]:after, +.radio input[type="radio"]:after, +.radio-inline input[type="radio"]:after { + content: ""; + display: block; + width: 18px; + height: 18px; + border-radius: 50%; + transition: 240ms; +} +input[type="radio"]:before, +.radio input[type="radio"]:before, +.radio-inline input[type="radio"]:before { + position: absolute; + left: 0; + top: -3px; + background-color: #2196f3; + -webkit-transform: scale(0); + transform: scale(0); +} +input[type="radio"]:after, +.radio input[type="radio"]:after, +.radio-inline input[type="radio"]:after { + position: relative; + top: -3px; + border: 2px solid #666666; +} +input[type="radio"]:checked:before, +.radio input[type="radio"]:checked:before, +.radio-inline input[type="radio"]:checked:before { + -webkit-transform: scale(0.5); + transform: scale(0.5); +} +input[type="radio"]:disabled:checked:before, +.radio input[type="radio"]:disabled:checked:before, +.radio-inline input[type="radio"]:disabled:checked:before { + background-color: #bbbbbb; +} +input[type="radio"]:checked:after, +.radio input[type="radio"]:checked:after, +.radio-inline input[type="radio"]:checked:after { + border-color: #2196f3; +} +input[type="radio"]:disabled:after, +.radio input[type="radio"]:disabled:after, +.radio-inline input[type="radio"]:disabled:after, +input[type="radio"]:disabled:checked:after, +.radio input[type="radio"]:disabled:checked:after, +.radio-inline input[type="radio"]:disabled:checked:after { + border-color: #bbbbbb; +} +input[type="checkbox"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + position: relative; + border: none; + margin-bottom: -4px; + -webkit-appearance: none; + appearance: none; + cursor: pointer; +} +input[type="checkbox"]:focus, +.checkbox input[type="checkbox"]:focus, +.checkbox-inline input[type="checkbox"]:focus { + outline: none; +} +input[type="checkbox"]:focus:after, +.checkbox input[type="checkbox"]:focus:after, +.checkbox-inline input[type="checkbox"]:focus:after { + border-color: #2196f3; +} +input[type="checkbox"]:after, +.checkbox input[type="checkbox"]:after, +.checkbox-inline input[type="checkbox"]:after { + content: ""; + display: block; + width: 18px; + height: 18px; + margin-top: -2px; + margin-right: 5px; + border: 2px solid #666666; + border-radius: 2px; + transition: 240ms; +} +input[type="checkbox"]:checked:before, +.checkbox input[type="checkbox"]:checked:before, +.checkbox-inline input[type="checkbox"]:checked:before { + content: ""; + position: absolute; + top: 0; + left: 6px; + display: table; + width: 6px; + height: 12px; + border: 2px solid #fff; + border-top-width: 0; + border-left-width: 0; + -webkit-transform: rotate(45deg); + transform: rotate(45deg); +} +input[type="checkbox"]:checked:after, +.checkbox input[type="checkbox"]:checked:after, +.checkbox-inline input[type="checkbox"]:checked:after { + background-color: #2196f3; + border-color: #2196f3; +} +input[type="checkbox"]:disabled:after, +.checkbox input[type="checkbox"]:disabled:after, +.checkbox-inline input[type="checkbox"]:disabled:after { + border-color: #bbbbbb; +} +input[type="checkbox"]:disabled:checked:after, +.checkbox input[type="checkbox"]:disabled:checked:after, +.checkbox-inline input[type="checkbox"]:disabled:checked:after { + background-color: #bbbbbb; + border-color: transparent; +} +.has-warning input:not([type=checkbox]), +.has-warning .form-control, +.has-warning input.form-control[readonly], +.has-warning input[type=text][readonly], +.has-warning [type=text].form-control[readonly], +.has-warning input:not([type=checkbox]):focus, +.has-warning .form-control:focus { + border-bottom: none; + box-shadow: inset 0 -2px 0 #ff9800; +} +.has-error input:not([type=checkbox]), +.has-error .form-control, +.has-error input.form-control[readonly], +.has-error input[type=text][readonly], +.has-error [type=text].form-control[readonly], +.has-error input:not([type=checkbox]):focus, +.has-error .form-control:focus { + border-bottom: none; + box-shadow: inset 0 -2px 0 #e51c23; +} +.has-success input:not([type=checkbox]), +.has-success .form-control, +.has-success input.form-control[readonly], +.has-success input[type=text][readonly], +.has-success [type=text].form-control[readonly], +.has-success input:not([type=checkbox]):focus, +.has-success .form-control:focus { + border-bottom: none; + box-shadow: inset 0 -2px 0 #4caf50; +} +.has-warning .input-group-addon, +.has-error .input-group-addon, +.has-success .input-group-addon { + color: #666666; + border-color: transparent; + background-color: transparent; +} +.form-group-lg select, +.form-group-lg select.form-control { + line-height: 1.5; +} +.nav-tabs > li > a, +.nav-tabs > li > a:focus { + margin-right: 0; + background-color: transparent; + border: none; + color: #666666; + box-shadow: inset 0 -1px 0 #dddddd; + transition: all 0.2s; +} +.nav-tabs > li > a:hover, +.nav-tabs > li > a:focus:hover { + background-color: transparent; + box-shadow: inset 0 -2px 0 #2196f3; + color: #2196f3; +} +.nav-tabs > li.active > a, +.nav-tabs > li.active > a:focus { + border: none; + box-shadow: inset 0 -2px 0 #2196f3; + color: #2196f3; +} +.nav-tabs > li.active > a:hover, +.nav-tabs > li.active > a:focus:hover { + border: none; + color: #2196f3; +} +.nav-tabs > li.disabled > a { + box-shadow: inset 0 -1px 0 #dddddd; +} +.nav-tabs.nav-justified > li > a, +.nav-tabs.nav-justified > li > a:hover, +.nav-tabs.nav-justified > li > a:focus, +.nav-tabs.nav-justified > .active > a, +.nav-tabs.nav-justified > .active > a:hover, +.nav-tabs.nav-justified > .active > a:focus { + border: none; +} +.nav-tabs .dropdown-menu { + margin-top: 0; +} +.dropdown-menu { + margin-top: 0; + border: none; + box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); +} +.alert { + border: none; + color: #fff; +} +.alert-success { + background-color: #4caf50; +} +.alert-info { + background-color: #9c27b0; +} +.alert-warning { + background-color: #ff9800; +} +.alert-danger { + background-color: #e51c23; +} +.alert a:not(.close):not(.btn), +.alert .alert-link { + color: #fff; + font-weight: bold; +} +.alert .close { + color: #fff; +} +.badge { + padding: 4px 6px 4px; +} +.progress { + position: relative; + z-index: 1; + height: 6px; + border-radius: 0; + box-shadow: none; +} +.progress-bar { + box-shadow: none; +} +.progress-bar:last-child { + border-radius: 0 3px 3px 0; +} +.progress-bar:last-child:before { + display: block; + content: ""; + position: absolute; + width: 100%; + height: 100%; + left: 0; + right: 0; + z-index: -1; + background-color: #cae6fc; +} +.progress-bar-success:last-child.progress-bar:before { + background-color: #c7e7c8; +} +.progress-bar-info:last-child.progress-bar:before { + background-color: #edc9f3; +} +.progress-bar-warning:last-child.progress-bar:before { + background-color: #ffe0b3; +} +.progress-bar-danger:last-child.progress-bar:before { + background-color: #f28e92; +} +.close { + font-size: 34px; + font-weight: 300; + line-height: 24px; + opacity: 0.6; + transition: all 0.2s; +} +.close:hover { + opacity: 1; +} +.list-group-item { + padding: 15px; +} +.list-group-item-text { + color: #bbbbbb; +} +.well { + border-radius: 0; + box-shadow: none; +} +.panel { + border: none; + border-radius: 2px; + box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); +} +.panel-heading { + border-bottom: none; +} +.panel-footer { + border-top: none; +} +.popover { + border: none; + box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); +} +.carousel-caption h1, +.carousel-caption h2, +.carousel-caption h3, +.carousel-caption h4, +.carousel-caption h5, +.carousel-caption h6 { + color: inherit; +} diff --git a/inst/assets/pkgdown.css b/inst/assets/pkgdown.css new file mode 100644 index 0000000..9145958 --- /dev/null +++ b/inst/assets/pkgdown.css @@ -0,0 +1,256 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; + color: #666; + display: flex; + flex-shrink: 0; +} +footer p { + margin-bottom: 0; +} +footer div { + flex: 1; +} +footer .pkgdown { + text-align: right; +} +footer p { + margin-bottom: 0; +} + +img.icon { + float: right; +} + +img { + max-width: 100%; +} + +/* Fix bug in bootstrap (only seen in firefox) */ +summary { + display: list-item; +} + +/* Typographic tweaking ---------------------------------*/ + +.contents .page-header { + margin-top: calc(-60px + 1em); +} + +/* Section anchors ---------------------------------*/ + +a.anchor { + margin-left: -30px; + display:inline-block; + width: 30px; + height: 30px; + visibility: hidden; + + background-image: url(./link.svg); + background-repeat: no-repeat; + background-size: 20px 20px; + background-position: center center; +} + +.hasAnchor:hover a.anchor { + visibility: visible; +} + +@media (max-width: 767px) { + .hasAnchor:hover a.anchor { + visibility: hidden; + } +} + + +/* Fixes for fixed navbar --------------------------*/ + +.contents h1, .contents h2, .contents h3, .contents h4 { + padding-top: 60px; + margin-top: -40px; +} + +/* Sidebar --------------------------*/ + +#sidebar { + margin-top: 30px; + position: -webkit-sticky; + position: sticky; + top: 70px; +} +#sidebar h2 { + font-size: 1.5em; + margin-top: 1em; +} + +#sidebar h2:first-child { + margin-top: 0; +} + +#sidebar .list-unstyled li { + margin-bottom: 0.5em; +} + +.orcid { + height: 16px; + /* margins are required by official ORCID trademark and display guidelines */ + margin-left:4px; + margin-right:4px; + vertical-align: middle; +} + +/* Reference index & topics ----------------------------------------------- */ + +.ref-index th {font-weight: normal;} + +.ref-index td {vertical-align: top;} +.ref-index .icon {width: 40px;} +.ref-index .alias {width: 40%;} +.ref-index-icons .alias {width: calc(40% - 40px);} +.ref-index .title {width: 60%;} + +.ref-arguments th {text-align: right; padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + +/* Syntax highlighting ---------------------------------------------------- */ + +pre { + word-wrap: normal; + word-break: normal; + border: 1px solid #eee; +} + +pre, code { + background-color: #f8f8f8; + color: #333; +} + +pre code { + overflow: auto; + word-wrap: normal; + white-space: pre; +} + +pre .img { + margin: 5px 0; +} + +pre .img img { + background-color: #fff; + display: block; + height: auto; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.message { color: black; font-weight: bolder;} +.error { color: orange; font-weight: bolder;} +.warning { color: #6A0366; font-weight: bolder;} + +/* Clipboard --------------------------*/ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; +} + +/* headroom.js ------------------------ */ + +.headroom { + will-change: transform; + transition: transform 200ms linear; +} +.headroom--pinned { + transform: translateY(0%); +} +.headroom--unpinned { + transform: translateY(-100%); +} + +/* mark.js ----------------------------*/ + +mark { + background-color: rgba(255, 255, 51, 0.5); + border-bottom: 2px solid rgba(255, 153, 51, 0.3); + padding: 1px; +} + +/* vertical spacing after htmlwidgets */ +.html-widget { + margin-bottom: 10px; +} + +/* fontawesome ------------------------ */ + +.fab { + font-family: "Font Awesome 5 Brands" !important; +} + +/* don't display links in code chunks when printing */ +/* source: https://stackoverflow.com/a/10781533 */ +@media print { + code a:link:after, code a:visited:after { + content: ""; + } +} diff --git a/inst/assets/pkgdown.js b/inst/assets/pkgdown.js new file mode 100644 index 0000000..087a762 --- /dev/null +++ b/inst/assets/pkgdown.js @@ -0,0 +1,113 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('.navbar-fixed-top').headroom(); + + $('body').css('padding-top', $('.navbar').height() + 10); + $(window).resize(function(){ + $('body').css('padding-top', $('.navbar').height() + 10); + }); + + $('body').scrollspy({ + target: '#sidebar', + offset: 60 + }); + + $('[data-toggle="tooltip"]').tooltip(); + + var cur_path = paths(location.pathname); + var links = $("#navbar ul li a"); + var max_length = -1; + var pos = -1; + for (var i = 0; i < links.length; i++) { + if (links[i].getAttribute("href") === "#") + continue; + // Ignore external links + if (links[i].host !== location.host) + continue; + + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } + } + + // Add class to parent
  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $(".examples, div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/inst/templates/navbar.html b/inst/templates/navbar.html new file mode 100644 index 0000000..6dec913 --- /dev/null +++ b/inst/templates/navbar.html @@ -0,0 +1,38 @@ +{{#navbar}} + +{{/navbar}} diff --git a/man/.DS_Store b/man/.DS_Store index 562a9340a8ac8068b3343fb399e6576c91fc1da5..dba8243b5dab548d83a0c6985347a19df319498a 100644 GIT binary patch literal 8196 zcmeHMT}&NC6g~%NncWsyY7zGK?*>94Ep35T2yLyGzqVMLl3pnEm+rm0g^kPZ_3qwF z3&o&`#s^d5Ph$LgX-s@T?Nbx&i$3@pjn&xr7xh73d^8%3KGicb3!z+y`l2T6Br|7b z&N+8xzWHW`vj6~g=FDb*Y5-uAxztusu}I>2o(ob@*rSj{@(0L*1%9vNrBhCUb<`0d z5FrpD5FrpD5Fzk?K!DzCp4bxazSKr#gg}JAvP6L24{^#|CPFzarMNn%5Fr4`66zL- z>J$eEpLi$}p`4adxYBRR=mA4l3_}c*?xc@%bCQWrPD?4>0i`=&I5LJ23X0LmFD}dh z6H-QHgg}HqAp$&hFNJep!wBT&$M0T>H2g5_q1MFhWoWRoxN8`iX(ZymMB!OmOVA z*{kY9v|nSiM85(X!|p_fuAku}-)s26k&NZitlp>L#p*Wc2N*lfwH>BOv#L!r$lVq& z)Fzr_OQDxahwbz(9fVW#y(7M{Zq%;0&Pz)8G_591|# z0-waE@CBT~SMfExg0JHncn#mickpBU48M^RESjbD;dx}GDt=4Fg0oJf4Nq^;nZ-yO z>)C(cKO*f{t0hx!Ut77cwqaXK+un}39bK@8^T{L_Bq<`OGfNGziMQ~P@46mar*2q^ z(*wjj>pbK1(MqLtJXXVqF`A%^9;=JhGorOtB(&<57+D%jE0)GP)MiFB)mBLDF11x5 zB5Ng5+pg|m#8+*l)b3Lg3O{NZ$aY;!QP@A{;ANPBYw#|7NZk7zzJnj&C-?&qOR+-m zZXK@2jaZKjxCOW3Hf+T4`MH|upb9-5Qp#tvCtGOv@wf5<}goed=MWZ zEfhHQKqU&PBevw(ZO3%K`6A@|0-?(XpAU@tq@8#e#`R>xvi=HIZ&jQ-X2oKI$5 z$8$$$gxT|P>lBw?t5`0%n#cb=OMm}ge!xU4ju2Qz2w+Kfvb&QOywt_WW9>NQvy^$_ zcGFS{7b>_3?-$`X$+>?RQav8(5}}-yk~Eb5^$!7ae~-#%IC}r1_g@fknH7Hmr_?^8 delta 117 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{MGjUEV6q~50$jH7iU^gQp`(_>iPsYt{VvUR& z8w{BjvvY6=G6R(XfdDs*$MEVenGXAUy}YB>>g diff --git a/man/concurve-package.Rd b/man/concurve-package.Rd index 918695c..e883c24 100644 --- a/man/concurve-package.Rd +++ b/man/concurve-package.Rd @@ -30,9 +30,9 @@ Allows one to compute consonance (confidence) Useful links: \itemize{ \item \url{https://data.lesslikely.com/concurve/} - \item \url{https://github.com/Zadchow/concurve} + \item \url{https://github.com/zadchow/concurve} \item \url{https://lesslikely.com/} - \item Report bugs at \url{https://github.com/Zadchow/concurve/issues} + \item Report bugs at \url{https://github.com/zadchow/concurve/issues} } } diff --git a/man/curve_boot.Rd b/man/curve_boot.Rd new file mode 100644 index 0000000..154012c --- /dev/null +++ b/man/curve_boot.Rd @@ -0,0 +1,67 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_boot.R +\name{curve_boot} +\alias{curve_boot} +\title{Generate Consonance Functions via Bootstrapping} +\usage{ +curve_boot( + data = data, + func = func, + method = "bca", + replicates = 2000, + steps = 1000, + table = TRUE +) +} +\arguments{ +\item{data}{Dataset that is being used to create a consonance function.} + +\item{func}{Custom function that is used to create parameters of interest that +will be bootstrapped.} + +\item{method}{The boostrap method that will be used to generate the functions. +Methods include "bca" which is the default and "t".} + +\item{replicates}{Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\value{ +A list with the dataframe of values in the first list and the table +in the second if table = TRUE. +} +\description{ +Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages +to generate consonance distrbutions. +} +\examples{ + +\donttest{ +data(diabetes, package = "bcaboot") +Xy <- cbind(diabetes$x, diabetes$y) +rfun <- function(Xy) { + y <- Xy[, 11] + X <- Xy[, 1:10] + return(summary(lm(y ~ X))$adj.r.squared) +} + +x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 200, steps = 1000) + +ggcurve(data = x[[1]]) +ggcurve(data = x[[3]]) + +plot_compare(x[[1]], x[[3]]) +} + +} diff --git a/man/curve_compare.Rd b/man/curve_compare.Rd new file mode 100644 index 0000000..85b775e --- /dev/null +++ b/man/curve_compare.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_compare.R +\name{curve_compare} +\alias{curve_compare} +\title{Compares two functions and produces an AUC score to show the amount of consonance.} +\usage{ +curve_compare(data1, data2, type = "c", plot = TRUE, ...) +} +\arguments{ +\item{data1}{The first dataframe produced by one of the interval functions +in which the intervals are stored.} + +\item{data2}{The second dataframe produced by one of the interval functions in +which the intervals are stored.} + +\item{type}{Choose whether to plot a "consonance" function, a "surprisal" function or +"likelihood". The default option is set to "c". The type must be set in quotes, +for example curve_compare (type = "s") or curve_compare(type = "c"). Other options +include "pd" for the consonance distribution function, and "cd" for the consonance +density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" +for likelihood and "d" for deviance function.} + +\item{plot}{by default it is set to TRUE and will use the plot_compare() function +to plot the two functions.} + +\item{...}{Can be used to pass further arguments to plot_compare().} +} +\description{ +Compares the p-value/s-value, and likelihood functions and computes an AUC number. +} +\examples{ +\donttest{ +library(concurve) +GroupA <- rnorm(50) +GroupB <- rnorm(50) +RandomData <- data.frame(GroupA, GroupB) +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) +GroupA2 <- rnorm(50) +GroupB2 <- rnorm(50) +RandomData2 <- data.frame(GroupA2, GroupB2) +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2") +(curve_compare(intervalsdf[[1]], randomframe[[1]])) +(curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")) +} + +} diff --git a/man/curve_corr.Rd b/man/curve_corr.Rd new file mode 100644 index 0000000..bbe100f --- /dev/null +++ b/man/curve_corr.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_corr.R +\name{curve_corr} +\alias{curve_corr} +\title{Computes Consonance Intervals for Correlations} +\usage{ +curve_corr(x, y, alternative, method, steps = 10000, table = TRUE) +} +\arguments{ +\item{x}{A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.} + +\item{y}{A vector that contains the data for one of the variables that will +be analyzed for correlational analysis.} + +\item{alternative}{Indicates the alternative hypothesis and must be one of "two.sided", +"greater" or "less". You can specify just the initial letter. "greater" corresponds to +positive association, "less" to negative association.} + +\item{method}{A character string indicating which correlation coefficient is +to be used for the test. One of "pearson", "kendall", or "spearman", +can be abbreviated.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Computes consonance intervals to produce P- and S-value functions for +correlational analysesusing the cor.test function in base R and places the +interval limits for each interval levelinto a data frame along with the +corresponding p-values and s-values. +} +\examples{ + +GroupA <- rnorm(50) +GroupB <- rnorm(50) +joe <- curve_corr(x = GroupA, y = GroupB, alternative = "two.sided", method = "pearson") +tibble::tibble(joe[[1]]) +} diff --git a/man/curve_gen.Rd b/man/curve_gen.Rd new file mode 100644 index 0000000..16be64d --- /dev/null +++ b/man/curve_gen.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_gen.R +\name{curve_gen} +\alias{curve_gen} +\title{General Consonance Functions Using Profile Likelihood, Wald, +or the bootstrap method for linear models.} +\usage{ +curve_gen(model, var, method = "wald", steps = 1000, table = TRUE) +} +\arguments{ +\item{model}{The statistical model of interest +(ANOVA, regression, logistic regression) is to be indicated here.} + +\item{var}{The variable of interest from the model (coefficients, intercept) +for which the intervals are to be produced.} + +\item{method}{Chooses the method to be used to calculate the +consonance intervals. There are currently four methods: +"default", "wald", "lm", and "boot". The "default" method uses the profile +likelihood method to compute intervals and can be used for models created by +the 'lm' function. The "wald" method is typicallywhat most people are +familiar with when computing intervals based on the calculated standard error. +The "lm" method allows this function to be used for specific scenarios like +logistic regression and the 'glm' function. The "boot" method allows for +bootstrapping at certain levels.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Computes thousands of consonance (confidence) intervals for +the chosen parameter in the selected model +(ANOVA, ANCOVA, regression, logistic regression) and places +the interval limits for each interval level into a data frame along +with the corresponding p-values and s-values. +} +\examples{ + +\donttest{ +# Simulate random data +GroupA <- rnorm(50) +GroupB <- rnorm(50) +RandomData <- data.frame(GroupA, GroupB) +rob <- lm(GroupA ~ GroupB, data = RandomData) +bob <- curve_gen(rob, "GroupB") +tibble::tibble(bob[[1]]) +} + +} diff --git a/man/curve_lik.Rd b/man/curve_lik.Rd new file mode 100644 index 0000000..2c37e48 --- /dev/null +++ b/man/curve_lik.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_lik.R +\name{curve_lik} +\alias{curve_lik} +\title{Compute the Profile Likelihood Functions} +\usage{ +curve_lik(likobject, data, table = TRUE) +} +\arguments{ +\item{likobject}{An object from the ProfileLikelihood package} + +\item{data}{The dataframe that was used to create the likelihood +object in the ProfileLikelihood package.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Compute the Profile Likelihood Functions +} +\examples{ + +library(ProfileLikelihood) +data(dataglm) +xx <- profilelike.glm(y ~ x1 + x2, dataglm, profile.theta = "group", binomial("logit")) +lik <- curve_lik(xx, dataglm) +tibble::tibble(lik[[1]]) +} diff --git a/man/curve_mean.Rd b/man/curve_mean.Rd new file mode 100644 index 0000000..a51a5da --- /dev/null +++ b/man/curve_mean.Rd @@ -0,0 +1,62 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_mean.R +\name{curve_mean} +\alias{curve_mean} +\title{Mean Interval Consonance Function} +\usage{ +curve_mean( + x, + y, + data, + paired = F, + method = "default", + replicates = 1000, + steps = 10000, + table = TRUE +) +} +\arguments{ +\item{x}{Variable that contains the data for the first group being compared.} + +\item{y}{Variable that contains the data for the second group being compared.} + +\item{data}{Data frame from which the variables are being extracted from.} + +\item{paired}{Indicates whether the statistical test is a paired difference test. +By default, it is set to "F",which means the function will be an unpaired +statistical test comparing two independent groups.Inserting "paired" will +change the test to a paired difference test.} + +\item{method}{By default this is turned off (set to "default"), but +allows for bootstrapping if "boot" is insertedinto the function call.} + +\item{replicates}{Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Computes thousands of consonance (confidence) intervals for the chosen +parameter in a statistical test that compares means and places the interval +limits for each interval level into a data frame along with the corresponding +p-values and s-values. +} +\examples{ + +# Simulate random data +GroupA <- runif(100, min = 0, max = 100) +GroupB <- runif(100, min = 0, max = 100) +RandomData <- data.frame(GroupA, GroupB) +bob <- curve_mean(GroupA, GroupB, RandomData) +tibble::tibble(bob[[1]]) +} diff --git a/man/curve_meta.Rd b/man/curve_meta.Rd new file mode 100644 index 0000000..f3a6eed --- /dev/null +++ b/man/curve_meta.Rd @@ -0,0 +1,92 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_meta.R +\name{curve_meta} +\alias{curve_meta} +\title{Meta-analytic Consonance Function} +\usage{ +curve_meta(x, measure = "default", steps = 10000, table = TRUE) +} +\arguments{ +\item{x}{Object where the meta-analysis parameters are stored, typically a +list produced by 'metafor'} + +\item{measure}{Indicates whether the object has a log transformation or is normal/default. +The default setting is "default. If the measure is set to "ratio", it will take +logarithmically transformed values and convert them back to normal values in the dataframe. +This is typically a setting used for binary outcomes such as risk ratios, +hazard ratios, and odds ratios.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Computes thousands of consonance (confidence) intervals for the chosen +parameter in the meta-analysis done by the metafor package and places the +interval limits for each interval level into a data frame along with the +corresponding p-values and s-values. +} +\examples{ + +# Simulate random data for two groups in two studies +GroupAData <- runif(20, min = 0, max = 100) +GroupAMean <- round(mean(GroupAData), digits = 2) +GroupASD <- round(sd(GroupAData), digits = 2) + +GroupBData <- runif(20, min = 0, max = 100) +GroupBMean <- round(mean(GroupBData), digits = 2) +GroupBSD <- round(sd(GroupBData), digits = 2) + +GroupCData <- runif(20, min = 0, max = 100) +GroupCMean <- round(mean(GroupCData), digits = 2) +GroupCSD <- round(sd(GroupCData), digits = 2) + +GroupDData <- runif(20, min = 0, max = 100) +GroupDMean <- round(mean(GroupDData), digits = 2) +GroupDSD <- round(sd(GroupDData), digits = 2) + +# Combine the data + +StudyName <- c("Study1", "Study2") +MeanTreatment <- c(GroupAMean, GroupCMean) +MeanControl <- c(GroupBMean, GroupDMean) +SDTreatment <- c(GroupASD, GroupCSD) +SDControl <- c(GroupBSD, GroupDSD) +NTreatment <- c(20, 20) +NControl <- c(20, 20) + +metadf <- data.frame( + StudyName, MeanTreatment, MeanControl, + SDTreatment, SDControl, NTreatment, NControl +) + +# Use metafor to calculate the standardized mean difference + +library(metafor) + +dat <- escalc( + measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, + n1i = NTreatment, m2i = MeanControl, sd2i = SDControl, + n2i = NControl, data = metadf +) + +# Pool the data using a particular method. Here "FE" is the fixed-effects model + +res <- rma(yi, vi, + data = dat, slab = paste(StudyName, sep = ", "), + method = "FE", digits = 2 +) + +# Calculate the intervals using the metainterval function + +metaf <- curve_meta(res) + +tibble::tibble(metaf[[1]]) +} diff --git a/man/curve_rev.Rd b/man/curve_rev.Rd new file mode 100644 index 0000000..6ba3cc0 --- /dev/null +++ b/man/curve_rev.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_rev.R +\name{curve_rev} +\alias{curve_rev} +\title{Reverse Engineer Consonance / Likelihood Functions Using the Point +Estimate and Confidence Limits} +\usage{ +curve_rev( + point, + LL, + UL, + type = "c", + measure = "default", + steps = 10000, + table = TRUE +) +} +\arguments{ +\item{point}{The point estimate from an analysis. Ex: 1.20} + +\item{LL}{The lower confidence limit from an analysis Ex: 1.0} + +\item{UL}{The upper confidence limit from an analysis Ex: 1.4} + +\item{type}{Indicates whether the produced result should be a consonance +function or a likelihood function. The default is "c" for consonance and +likelihood can be set via "l".} + +\item{measure}{The type of data being used. If they involve mean differences,} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Using the confidence limits and point estimates from a dataset, one can use +these estimates to compute thousands of consonance intervals and graph the +intervals to form a consonance and surprisal function. +} +\examples{ + +# From a real published study. Point estimate of the result was hazard ratio of 1.61 and +# lower bound of the interval is 0.997 while upper bound of the interval is 2.59. +# +df <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio") + +tibble::tibble(df[[1]]) +} diff --git a/man/curve_surv.Rd b/man/curve_surv.Rd new file mode 100644 index 0000000..2f91d5b --- /dev/null +++ b/man/curve_surv.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_surv.R +\name{curve_surv} +\alias{curve_surv} +\title{Produce Consonance Intervals for Survival Data} +\usage{ +curve_surv(data, x, steps = 10000, table = TRUE) +} +\arguments{ +\item{data}{Object where the Cox model is stored, typically a list produced by the +'survival' package.} + +\item{x}{Predictor of interest within the survival model for which the +consonance intervals should be computed.} + +\item{steps}{Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.} + +\item{table}{Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.} +} +\description{ +Computes thousands of consonance (confidence) intervals for the chosen +parameter in the Cox model computed by the 'survival' package and places +the interval limits for each interval level into a data frame along +with the corresponding p-value and s-value. +} diff --git a/man/curve_table.Rd b/man/curve_table.Rd new file mode 100644 index 0000000..0d0014a --- /dev/null +++ b/man/curve_table.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/curve_table.R +\name{curve_table} +\alias{curve_table} +\title{Produce Tables For concurve Functions} +\usage{ +curve_table(data, levels, type = "c", format = "data.frame") +} +\arguments{ +\item{data}{Dataframe from a concurve function to produce a table for} + +\item{levels}{Levels of the consonance intervals or likelihood intervals that should be +included in the table.} + +\item{type}{Indicates whether the table is for a consonance function or likelihood function. +The default is set to "c" for consonance and can be switched to "l" for likelihood.} + +\item{format}{The format of the tables. The options include "data.frame" which is the +default, "tibble", "docx" (which creates a table for a word document), "pptx" (which +creates a table for powerpoint), "latex", (which creates a table for a TeX document), and +"image", which produces an image of the table.} +} +\description{ +Produces publication-ready tables with relevant statistics of interest +for functions produced from the concurve package. +} +\examples{ + +library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + +intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData, method = "default") + +(z <- curve_table(intervalsdf[[1]], format = "data.frame")) +(z <- curve_table(intervalsdf[[1]], format = "tibble")) +(z <- curve_table(intervalsdf[[1]], format = "latex")) +} diff --git a/man/defunct.Rd b/man/defunct.Rd index 223049e..1febda9 100644 --- a/man/defunct.Rd +++ b/man/defunct.Rd @@ -9,6 +9,8 @@ \alias{survintervals} \alias{likintervals} \alias{rev_eng} +\alias{ggconcurve} +\alias{plot_concurve} \title{Deprecated functions in \pkg{concurve}.} \usage{ plotpint(...) @@ -26,18 +28,34 @@ corrintervals(...) survintervals(...) rev_eng(...) + +ggconcurve(...) + +plot_concurve(...) + } + \description{ The functions listed below are deprecated. Please use the listed alternatives.} \section{\code{plotpint}}{ -For \code{plotpint}, use \code{\link{ggconcurve}}. +For \code{plotpint}, use \code{\link{ggcurve}}. } \section{\code{plotsint}}{ -For \code{plotsint}, use \code{\link{ggconcurve}}. +For \code{plotsint}, use \code{\link{ggcurve}}. +} + +\section{\code{plot_concurve}}{ + +For \code{plot_concurve}, use \code{\link{ggcurve}}. +} + +\section{\code{ggconcurve}}{ + +For \code{ggconcurve}, use \code{\link{ggcurve}}. } \section{\code{meanintervals}}{ diff --git a/man/figures/.DS_Store b/man/figures/.DS_Store index 08621ff5dd4adf09d2bf1b94b7a84d4399a7db60..27902fb4320507c668f17e3188b6c744e90aeb71 100644 GIT binary patch delta 82 zcmZoMXfc=|#>CJ*u~2NHo}wrd0|Nsi1A_nqgC0WxLmorAXHI_d#6tDS1|lq*vzh0x mY<|FehiNlA2R{c;)8>oJ-S5S~eUh=|ae9{1=;sDBp`La7JOzJRu35rPr4o^$bGd>S9d2heYJhGLUV zgBKB*f!S|&cD{ss+h#IE#FORim}o>qIb1N?!jLf87jIc+5!vK;jc2;2iXLc82d!)m z{6z)i>~`pmmN>OJeLufPL>8B2HJ_E$3|aCMIfL(iM#C({JjBVTm~XwzqcoFudzr@_ z%-3G#NeAk3wTnO?0rN26TLJ~U~t~Z$I{Iea0Og}A1i>GEtYR9 z`sfO{0VuAt{-ALaiQp=E8q&OD=^UINb3JZcmKam@|7##3j8YtcsRY9 zPVh)pTU!q&wKl-7;bM|kE3PP*#8Ql0DaCv6Xov@y01Lxf5fPYw2t*lta0Pxli>a:hover, .navbar-default .navbar-nav>li>a:focus { background-color: #eee; @@ -96,3 +100,5 @@ pre code { color: #eeeeee; background-color: #d46c5b; } + + diff --git a/tests/spelling.R b/tests/spelling.R index 6713838..13f77d9 100644 --- a/tests/spelling.R +++ b/tests/spelling.R @@ -1,3 +1,6 @@ -if(requireNamespace('spelling', quietly = TRUE)) - spelling::spell_check_test(vignettes = TRUE, error = FALSE, - skip_on_cran = TRUE) +if (requireNamespace("spelling", quietly = TRUE)) { + spelling::spell_check_test( + vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE + ) +} diff --git a/usethis.R b/usethis.R index 3776b7f..a46524e 100644 --- a/usethis.R +++ b/usethis.R @@ -23,15 +23,22 @@ use_package("scales", "Imports", min_version = NULL) use_package("testthat", "Suggests", min_version = NULL) use_package("covr", "Suggests", min_version = NULL) use_package("spelling", "Suggests", min_version = NULL) +use_package("Lock5Data", "Suggests", min_version = NULL) # Other helper functions use_build_ignore("usethis.R", escape = TRUE) +use_build_ignore("references.bib", escape = TRUE) +use_build_ignore("american-medical-association.csl", escape = TRUE) + use_spell_check(vignettes = TRUE, lang = "en-US", error = FALSE) use_cran_comments(open = interactive()) use_tidy_style() use_revdep() +check_rhub(pkg = ".", platforms = NULL, email = NULL, + interactive = TRUE, build_args = NULL) + check(pkg = ".", document = NA, build_args = NULL, manual = TRUE, cran = TRUE, remote = TRUE, incoming = TRUE, force_suggests = TRUE, run_dont_test = TRUE, args = "--timings", diff --git a/vignettes/american-medical-association.csl b/vignettes/american-medical-association.csl new file mode 100755 index 0000000..78e5311 --- /dev/null +++ b/vignettes/american-medical-association.csl @@ -0,0 +1,276 @@ + + diff --git a/vignettes/examples.Rmd b/vignettes/examples.Rmd index 5f91040..daa78eb 100644 --- a/vignettes/examples.Rmd +++ b/vignettes/examples.Rmd @@ -1,6 +1,9 @@ --- title: "Examples in R" output: rmarkdown::html_vignette +bibliography: references.bib +link-citations: yes +csl: american-medical-association.csl vignette: > %\VignetteIndexEntry{Examples in R} %\VignetteEngine{knitr::rmarkdown} @@ -9,326 +12,279 @@ vignette: > ```{r setup, include = FALSE} knitr::opts_chunk$set( - collapse = TRUE, - comment = "#>" + message = TRUE, + warning = TRUE, + collapse = TRUE, + comment = "#>" ) ``` -## Using Mean Differences +## Introduction -If we were to generate some random data from a normal distribution with -the following code, +Here I show how to produce _P_-value, _S_-value, likelihood, and deviance functions with the `concurve` package using fake data and data from real studies. Simply put, these functions are rich sources of information for scientific inference and the image below, taken from Xie & Singh, 2013[@xie2013isr] displays why. -``` r -GroupA <- rnorm(20) -GroupB <- rnorm(20) + +For a more extensive discussion of these concepts, see the following references. [@birnbaum1961ams; @chow2019asb; @fraser2017arsa; @fraser2019as; @Poole1987-nb; @poole1987ajph; @Schweder2002-vh; @schweder2016; @Singh2007-zr; @Sullivan1990-ha; @whitehead1993sm; @xie2013isr; @rothman2008me] + +To get started, we could generate some normal data and combine two vectors in a dataframe + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(concurve) +set.seed(1031) +GroupA <- rnorm(500) +GroupB <- rnorm(500) RandomData <- data.frame(GroupA, GroupB) ``` -and compare the means of these two "groups" using a t-test, +and look at the differences between the two vectors. We'll plug these vectors and the dataframe they're in inside of the `curve_mean()` function. Here, the default method involves calculating CIs using the Wald method. -```r -testresults <- t.test(GroupA, GroupB, data = RandomData, paired = FALSE) +``` {r} +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) ``` -we would likely see some differences, given that we have such a small -sample in each group. -```r -summary(testresults) +Each of the functions within `concurve` will generally produce a list with three items, and the first will usually contain the function of interest. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +tibble::tibble(intervalsdf[[1]]) ``` -We can see our P-value for the statistical test along with the computed -95% interval with the command above (which is given to us by default by the program). Thus, -effect sizes that range from the lower bound of this interval to the -upper bound are compatible with the test model at this consonance -level. +We can view the function using the `ggcurve()` function. The two basic arguments that must be provided are the data argument and the "type" argument. To plot a consonance function, we would write "c". -However, as stated before, a 95% interval is only an artifact of the -commonly used 5% alpha level for hypothesis testing and is nowhere near -as informative as a function. +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(function1 <- ggcurve(data = intervalsdf[[1]], type = "c")) +``` -If we were to take the information from this data and calculate a -P-value function where every single consonance interval and its -corresponding P-value were plotted, we would be able to see the full -range of effect sizes compatible with the test model at various levels. +We can see that the consonance "curve" is every interval estimate plotted, and provides the _P_-values, CIs, along with the median unbiased estimate It can be defined as such, -It is relatively easy to produce such a function using the -[**concurve**](https://github.com/Zadchow/concurve) -package in R. +$$C V_{n}(\theta)=1-2\left|H_{n}(\theta)-0.5\right|=2 \min \left\{H_{n}(\theta), 1-H_{n}(\theta)\right\}$$ -Install the package directly from [CRAN](https://cran.r-project.org/package=concurve) +Its information counterpart, the surprisal function, can be constructed by taking the $-log_{2}$ of the _P_-value.[@chow2019asb; @greenland2019as; @Shannon1948-uq] -``` r -install.packages("concurve") -``` -or get a more slightly up-to-date version via GitHub. +To view the surprisal function, we simply change the type to "s". -``` r -library(devtools) -install_github("zadchow/concurve") +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(function1 <- ggcurve(data = intervalsdf[[1]], type = "s")) ``` -We’ll use the same data from above to calculate a P-value function and -since we are focusing on mean differences using a t-test, we will use -the `curve_meta()` function to calculate our consonance -intervals and store them in a dataframe. -``` r -library(concurve) -intervalsdf <- curve_mean(GroupA, GroupB, - data = RandomData, method = "default" -) -``` +We can also view the consonance distribution by changing the type to "cdf", which is a cumulative probability distribution. The point at which the curve reaches 50% is known as the "median unbiased estimate". It is the same estimate that is typically at the peak of the _P_-value curve from above. -Now thousands of consonance intervals at various levels have been -stored in the dataframe “intervalsdf.” We can preview some of the entries -via the `tibble` package. -```r -tibble::tibble(intervalsdf) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf")) ``` -There's a quick look at the first few entries of our dataframe. +We can also get relevant statistics that show the range of values by using the `curve_table()` function. There are several formats that can be exported such as .docx, .ppt, and TeX. -We can plot this data using the `ggconcurve()` function. - -``` r -pfunction <- ggconcurve(type = "consonance", intervalsdf) -pfunction -``` -
    - -
    - -Now we can see every consonance interval and its corresponding -P-value and consonance level plotted. As stated before, a single 95% -consonance interval is simply a slice through this function, which -provides far more information as to what is compatible with the test -model and its assumptions. - -Furthermore, we can also produce a "surprisal" function by plotting every consonance interval and its corresponding -[***S-value***](https://lesslikely.com/statistics/s-values/) -using the `ggconcurve()` function with type being set as "surprisal". - -``` r -sfunction <- ggconcurve(type = "surprisal", intervalsdf) -sfunction +```{r echo=TRUE, fig.height=2, fig.width=4} +(x <- curve_table(data = intervalsdf[[1]], format = "image")) ``` -
    - -
    - -The graph from the code above provides us with consonance levels and the maximum -amount of information against the effect sizes contained in the -consonance interval. +# Comparing Functions -## Simple Linear Models +If we wanted to compare two studies to see the amount of "consonance", we could use the `curve_compare()` function to get a numerical output. -We can also try this with other simple linear models. +First, we generate some more fake data -Let’s simulate more normal data and fit a simple linear regression to it -using ordinary least squares regression with the base R `lm()` function. - -``` r +```{r echo=TRUE, fig.height=4.5, fig.width=6} GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) - RandomData2 <- data.frame(GroupA2, GroupB2) +model <- glm(GroupA2 ~ GroupB2, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000) +``` -model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +Once again, we'll plot this data with `ggcurve()`. We can also indicate whether we want certain interval estimates to be plotted in the function with the "levels" argument. If we wanted to plot the 50%, 75%, and 95% intervals, we'd provide the argument this way: -summary(model) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE)) ``` -We can see some of the basic statistics of our model including the 95% -interval for our predictor (GroupB). Perhaps we want more information. -Well we can do that! Using the `curve_gen()`, we can calculate several -consonance intervals for the regression coefficient and then plot the -consonance and surprisal functions. +Now that we have two datasets and two functions, we can compare them using the `curve_compare()` function. -``` r -randomframe <- curve_gen(model, "GroupB2") -tibble::tibble(randomframe) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(curve_compare( + data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "c", + plot = TRUE, measure = "default", nullvalue = TRUE +)) ``` -Now that we have our data frame, we can graph our function. +This function will provide us with the area that is shared between the curve, along with a ratio of overlap to non-overlap. -``` r -ggconcurve(type = "consonance", randomframe) +We can also do this for the surprisal function simply by changing type to "s". + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(curve_compare( + data1 = intervalsdf[[1]], data2 = randomframe[[1]], type = "s", + plot = TRUE, measure = "default", nullvalue = FALSE +)) ``` -
    - -
    +It's clear that the outputs have changed and indicate far more overlap than before. + +# Constructing Functions From Single Intervals -``` r -s <- ggconcurve(type = "surprisal", randomframe) -s +We can also take a set of confidence limits and use them to construct a consonance, surprisal, likelihood or deviance function using the `curve_rev()` function. + +Here, we'll use two epidemiological studies[@brown2017j; @brown2017jcp] that studied the impact of SSRI exposure in pregnant mothers, and the rate of autism in children. + +Both of these studies suggested a null effect of SSRI exposure on autism rates in children. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +curve1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "c", measure = "ratio", steps = 10000) +(ggcurve(data = curve1[[1]], type = "c", measure = "ratio", nullvalue = TRUE)) +curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "c", measure = "ratio", steps = 10000) +(ggcurve(data = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE)) ``` -
    - -
    +The null value is shown via the red line and it's clear that a large mass of the function is away from it. -We can also compare these functions to likelihood functions (also called -support intervals), and we’ll see that we get very similar results. -We’ll do this using the ***ProfileLikelihood*** package. +We can also see this by plotting the likelihood functions via the `curve_rev()` function. -``` r -xx <- profilelike.lm( - formula = GroupA2 ~ 1, data = RandomData2, - profile.theta = "GroupB2", - lo.theta = -0.3, hi.theta = 0.3, length = 500 -) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000) +(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) +lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000) +(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE)) ``` -Now we plot our likelihood function and we can see what the maximum -likelihood estimation is. Notice that it’s practically similar to the -interval in the S-value function with 0 bits of information against it -and and the consonance interval in the P-value function with a -P-value of 1. +We can also view the amount of agreement between the likelihood functions of these two studies. -``` r -profilelike.plot( - theta = xx$theta, - profile.lik.norm = xx$profile.lik.norm, round = 3 -) -title(main = "Likelihood Function") +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(plot_compare( + data1 = lik1[[1]], data2 = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) ``` -
    - -
    +and the consonance functions -We’ve used a relatively easy example for this blog post, but the -[**concurve**](https://github.com/Zadchow/concurve) -package is also able to calculate consonance functions for multiple -regressions, logistic regressions, ANOVAs, and meta-analyses (that have -been produced by the ***metafor*** package). +```{r echo=TRUE, fig.height=4.5, fig.width=6} +(plot_compare( + data1 = curve1[[1]], data2 = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE, title = "Brown et al. 2017. J Clin Psychiatry. vs. \nBrown et al. 2017. JAMA.", + subtitle = "J Clin Psychiatry: OR = 1.7, 1/6.83 LI: LL = 1.1, UL = 2.6 \nJAMA: HR = 1.61, 1/6.83 LI: LL = 0.997, UL = 2.59", xaxis = expression(Theta ~ "= Hazard Ratio / Odds Ratio") +)) +``` -## Using Meta-Analysis Data +# The Bootstrap and Consonance Functions -Here we present another quick example with a meta-analysis of simulated -data. +Some authors have shown that the bootstrap distribution is equal to the confidence distribution because it meets the definition of a consonance distribution.[@efron1994; @efron2018; @xie2013isr] The bootstrap distribution and the asymptotic consonance distribution would be defined as: -First, we generate random data for two groups in two hypothetical -studies +$$H_{n}(\theta)=1-P\left(\hat{\theta}-\hat{\theta}^{*} \leq \hat{\theta}-\theta | \mathbf{x}\right)=P\left(\hat{\theta}^{*} \leq \theta | \mathbf{x}\right)$$ -``` r -GroupAData <- runif(20, min = 0, max = 100) -GroupAMean <- round(mean(GroupAData), digits = 2) -GroupASD <- round(sd(GroupAData), digits = 2) +Certain bootstrap methods such as the BCa method and _t_-bootstrap method also yield second order accuracy of consonance distributions. -GroupBData <- runif(20, min = 0, max = 100) -GroupBMean <- round(mean(GroupBData), digits = 2) -GroupBSD <- round(sd(GroupBData), digits = 2) +$$H_{n}(\theta)=1-P\left(\frac{\hat{\theta}^{*}-\hat{\theta}}{\widehat{S E}^{*}\left(\hat{\theta}^{*}\right)} \leq \frac{\hat{\theta}-\theta}{\widehat{S E}(\hat{\theta})} | \mathbf{x}\right)$$ -GroupCData <- runif(20, min = 0, max = 100) -GroupCMean <- round(mean(GroupCData), digits = 2) -GroupCSD <- round(sd(GroupCData), digits = 2) +Here, I demonstrate how to use these particular bootstrap methods to arrive at consonance curves and densities. -GroupDData <- runif(20, min = 0, max = 100) -GroupDMean <- round(mean(GroupDData), digits = 2) -GroupDSD <- round(sd(GroupDData), digits = 2) +We'll use the Iris dataset and construct a function that'll yield a parameter of interest. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +iris <- datasets::iris +foo <- function(data, indices) { + dt <- data[indices, ] + c( + cor(dt[, 1], dt[, 2], method = "p") + ) +} ``` -We can then quickly combine the data in a dataframe. - -``` r -StudyName <- c("Study1", "Study2") -MeanTreatment <- c(GroupAMean, GroupCMean) -MeanControl <- c(GroupBMean, GroupDMean) -SDTreatment <- c(GroupASD, GroupCSD) -SDControl <- c(GroupBSD, GroupDSD) -NTreatment <- c(20, 20) -NControl <- c(20, 20) - -metadf <- data.frame( - StudyName, MeanTreatment, MeanControl, - SDTreatment, SDControl, - NTreatment, NControl -) +We can now use the `curve_boot()` method to construct a function. The default method used for this function is the "BCa" method provided by the [`bcaboot`](https://cran.r-project.org/package=bcaboot) package.[@efron2018] + +```{r include=FALSE} +y <- curve_boot(data = iris, func = foo, method = "bca", replicates = 2000, steps = 1000) ``` -Then, we’ll use ***metafor*** to calculate the standardized mean -difference. +I will suppress the output of the function because it is unnecessarily long. But we've placed all the estimates into a list object called y. -``` r -dat <- escalc( - measure = "SMD", - m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment, - m2i = MeanControl, sd2i = SDControl, n2i = NControl, - data = metadf -) +The first item in the list will be the consonance distribution constructed by typical means, while the third item will be the bootstrap approximation to the consonance distribution. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(data = y[[1]]) +ggcurve(data = y[[3]]) ``` -Next, we’ll pool the data using a ~~fixed-effects~~ common-effects model +We can also print out a table for TeX documents -``` r -res <- rma(yi, vi, - data = dat, slab = paste(StudyName, sep = ", "), - method = "FE", digits = 2 -) +```{r echo=TRUE, fig.height=2, fig.width=4} +(gg <- curve_table(data = y[[1]], format = "image")) ``` -Let’s look at our output. +More bootstrap replications will lead to a smoother function. But for now, we can compare these two functions to see how similar they are. -```r -res +```{r echo=TRUE, fig.height=4.5, fig.width=6} +plot_compare(y[[1]], y[[3]]) ``` +The densities can also be calculated accurately using the _t_-bootstrap method. Here we use a different dataset to show this + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(Lock5Data) +dataz <- data(CommuteAtlanta) +func = function(data, index) { + x <- as.numeric(unlist(data[1])) + y <- as.numeric(unlist(data[2])) + return(mean(x[index]) - mean(y[index])) +} ``` -Fixed-Effects Model (k = 2) -Test for Heterogeneity: -Q(df = 1) = 0.61, p-val = 0.44 +Our function is a simple mean difference. This time, we'll set the method to "t" for the _t_-bootstrap method -Model Results: +```{r echo=TRUE, fig.height=4.5, fig.width=6} +z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000) +ggcurve(data = z[[1]]) +ggcurve(data = z[[2]], type = "cd") +``` -estimate se zval pval ci.lb ci.ub - 0.16 0.22 0.73 0.47 -0.28 0.60 +The consonance curve and density are nearly identical. With more bootstrap replications, they are very likely to converge. ---- -Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 +```{r echo=TRUE, fig.height=2, fig.width=4} +(zz <- curve_table(data = z[[1]], format = "image")) ``` -Take a look at the pooled summary effect and its interval. Keep it in -mind as we move onto constructing a consonance function. +## Using Profile Likelihoods -We can now take the object produced by the meta-analysis and calculate a -P-value and S-value function with it to see the full spectrum of effect -sizes compatible with the test model at every level. We’ll use the -`curve_meta()` function to do this. +For this last example, we'll explore the `curve_lik()` function, which can help generate profile likelihood functions, and deviance statistics with the help of the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) package. -``` r -metaf <- curve_meta(res) -tibble::tibble(metaf) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +library(ProfileLikelihood) ``` -Now that we have our dataframe with every computed interval, we can plot -the functions. +We'll use a simple example taken directly from the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) documentation where we'll calculate the likelihoods from a glm model -``` r -ggconcurve(type = "consonance", metaf) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +data(dataglm) +xx <- profilelike.glm(y ~ x1 + x2, data=dataglm, profile.theta="group", +family=binomial(link="logit"), length=500, round=2) ``` -
    - -
    +Then, we’ll use `curve_lik()` on the object that the [`ProfileLikelihood`](https://cran.r-project.org/package=ProfileLikelihood) package created. + +```{r echo=TRUE, fig.height=4.5, fig.width=6} +lik <- curve_lik(xx, dataglm) +tibble::tibble(lik[[1]]) +``` -And our S-value function +Next, we’ll plot three functions, the relative likelihood, the log likelihood, the likelihoodm, and the deviance function. -``` r -ggconcurve(type = "surprisal", metaf) +```{r echo=TRUE, fig.height=4.5, fig.width=6} +ggcurve(lik[[1]], type = "l1") +ggcurve(lik[[1]], type = "l2") +ggcurve(lik[[1]], type = "l3") +ggcurve(lik[[1]], type = "d") ``` -
    - -
    - -Compare the span of these functions and the information they provide to -the consonance interval provided by the forest plot. We are now no -longer limited to interpreting an arbitrarily chosen interval by -mindless analytic decisions often built into statistical packages by -default. + +The obvious advantage of using reduced likelihoods is that they are free of nuisance parameters + +$$L_{t_{n}}(\theta)=f_{n}\left(F_{n}^{-1}\left(H_{p i v}(\theta)\right)\right)\left|\frac{\partial}{\partial t} \psi\left(t_{n}, \theta\right)\right|=h_{p i v}(\theta)\left|\frac{\partial}{\partial t} \psi(t, \theta)\right| /\left.\left|\frac{\partial}{\partial \theta} \psi(t, \theta)\right|\right|_{t=t_{n}}$$ +thus, giving summaries of the data that can be incorporated into combined analyses. + +* * * + +# References + +* * * diff --git a/vignettes/figures/densityfunction.png b/vignettes/figures/densityfunction.png new file mode 100644 index 0000000000000000000000000000000000000000..66d024c435936f48adc7e8466ce5f5071ef0ff0c GIT binary patch literal 123882 zcmeFZWmJ@H)CM{rC8&T>A|)+IsYpmE2-4k+(gM<57SbrGQcAZ-H;RDL9YacYcbH{QJ%g2 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    Small typos or grammatical errors in documentation may be edited directly using the GitHub web interface, so long as the changes are made in the source file.

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    +Prerequisites

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    Before you make a substantial pull request, you should always file an issue and make sure someone from the team agrees that it’s a problem. If you’ve found a bug, create an associated issue and illustrate the bug with a minimal reprex.

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                        GNU GENERAL PUBLIC LICENSE
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    + + + + + + + + + + + diff --git a/docs/SUPPORT.html b/docs/SUPPORT.html new file mode 100644 index 0000000..ec46a06 --- /dev/null +++ b/docs/SUPPORT.html @@ -0,0 +1,208 @@ + + + + + + + + +Getting help with concurve • concurve + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    + +

    Thanks for using concurve. Before filing an issue, there are a few places to explore and pieces to put together to make the process as smooth as possible.

    +

    Start by making a minimal reproducible example using the reprex package. If you haven’t heard of or used reprex before, you’re in for a treat! Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it’ll take you to learn what it’s all about). For additional reprex pointers, check out the Get help! section of the tidyverse site.

    +

    Armed with your reprex, the next step is to figure out where to ask.

    +
      +
    • If it’s a question: start with community.rstudio.com, and/or StackOverflow. There are more people there to answer questions.
      +
    • +
    • If it’s a bug: you’re in the right place, file an issue.
      +
    • +
    • If you’re not sure: let the community help you figure it out! If your problem is a bug or a feature request, you can easily return here and report it.
    • +
    +

    Before opening a new issue, be sure to search issues and pull requests to make sure the bug hasn’t been reported and/or already fixed in the development version. By default, the search will be pre-populated with is:issue is:open. You can edit the qualifiers (e.g. is:pr, is:closed) as needed. For example, you’d simply remove is:open to search all issues in the repo, open or closed.

    +

    If you are in the right place, and need to file an issue, please review the “File issues” paragraph from the tidyverse contributing guidelines.

    +

    Thanks for your help!

    +
    + +
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    + + + + +
    + + + + + + + + + + + diff --git a/docs/articles/examples.html b/docs/articles/examples.html new file mode 100644 index 0000000..e88482b --- /dev/null +++ b/docs/articles/examples.html @@ -0,0 +1,699 @@ + + + + + + + +Examples in R • concurve + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    + + + + +
    +

    +Introduction

    +

    Here I show how to produce P-value, S-value, likelihood, and deviance functions with the concurve package using fake data and data from real studies. Simply put, these functions are rich sources of information for scientific inference and the image below, taken from Xie & Singh, 20131 displays why.

    +

    +

    For a more extensive discussion of these concepts, see the following references.113

    +

    To get started, we could generate some normal data and combine two vectors in a dataframe

    +
    library(concurve)
    +set.seed(1031)
    +GroupA <- rnorm(500)
    +GroupB <- rnorm(500)
    +RandomData <- data.frame(GroupA, GroupB)
    +

    and look at the differences between the two vectors. We’ll plug these vectors and the dataframe they’re in inside of the curve_mean() function. Here, the default method involves calculating CIs using the Wald method.

    + +

    Each of the functions within concurve will generally produce a list with three items, and the first will usually contain the function of interest.

    + +

    We can view the function using the ggcurve() function. The two basic arguments that must be provided are the data argument and the “type” argument. To plot a consonance function, we would write “c”.

    +
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "c"))
    +

    +

    We can see that the consonance “curve” is every interval estimate plotted, and provides the P-values, CIs, along with the median unbiased estimate It can be defined as such,

    +

    \[C V_{n}(\theta)=1-2\left|H_{n}(\theta)-0.5\right|=2 \min \left\{H_{n}(\theta), 1-H_{n}(\theta)\right\}\]

    +

    Its information counterpart, the surprisal function, can be constructed by taking the \(-log_{2}\) of the P-value.3,14,15

    +

    To view the surprisal function, we simply change the type to “s”.

    +
    (function1 <- ggcurve(data = intervalsdf[[1]], type = "s"))
    +

    +

    We can also view the consonance distribution by changing the type to “cdf”, which is a cumulative probability distribution. The point at which the curve reaches 50% is known as the “median unbiased estimate”. It is the same estimate that is typically at the peak of the P-value curve from above.

    +
    (function1s <- ggcurve(data = intervalsdf[[2]], type = "cdf"))
    +

    +

    We can also get relevant statistics that show the range of values by using the curve_table() function. There are several formats that can be exported such as .docx, .ppt, and TeX.

    +
    (x <- curve_table(data = intervalsdf[[1]], format = "image"))
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Lower Limit

    Upper Limit

    Interval Width

    Interval Level (%)

    CDF

    P-value

    S-value (bits)

    -0.132

    -0.093

    0.039

    25.0

    0.625

    0.750

    0.415

    -0.154

    -0.071

    0.083

    50.0

    0.750

    0.500

    1.000

    -0.183

    -0.042

    0.142

    75.0

    0.875

    0.250

    2.000

    -0.192

    -0.034

    0.158

    80.0

    0.900

    0.200

    2.322

    -0.201

    -0.024

    0.177

    85.0

    0.925

    0.150

    2.737

    -0.214

    -0.011

    0.203

    90.0

    0.950

    0.100

    3.322

    -0.233

    0.008

    0.242

    95.0

    0.975

    0.050

    4.322

    -0.251

    0.026

    0.276

    97.5

    0.988

    0.025

    5.322

    -0.271

    0.046

    0.318

    99.0

    0.995

    0.010

    6.644

    +
    +
    +

    +Comparing Functions

    +

    If we wanted to compare two studies to see the amount of “consonance”, we could use the curve_compare() function to get a numerical output.

    +

    First, we generate some more fake data

    +
    GroupA2 <- rnorm(500)
    +GroupB2 <- rnorm(500)
    +RandomData2 <- data.frame(GroupA2, GroupB2)
    +model <- glm(GroupA2 ~ GroupB2, data = RandomData2)
    +randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000)
    +

    Once again, we’ll plot this data with ggcurve(). We can also indicate whether we want certain interval estimates to be plotted in the function with the “levels” argument. If we wanted to plot the 50%, 75%, and 95% intervals, we’d provide the argument this way:

    +
    (function2 <- ggcurve(type = "c", randomframe[[1]], levels = c(0.50, 0.75, 0.95), nullvalue = TRUE))
    +

    +

    Now that we have two datasets and two functions, we can compare them using the curve_compare() function.

    + +

    +

    This function will provide us with the area that is shared between the curve, along with a ratio of overlap to non-overlap.

    +

    We can also do this for the surprisal function simply by changing type to “s”.

    + +

    +

    It’s clear that the outputs have changed and indicate far more overlap than before.

    +
    +
    +

    +Constructing Functions From Single Intervals

    +

    We can also take a set of confidence limits and use them to construct a consonance, surprisal, likelihood or deviance function using the curve_rev() function.

    +

    Here, we’ll use two epidemiological studies16,17 that studied the impact of SSRI exposure in pregnant mothers, and the rate of autism in children.

    +

    Both of these studies suggested a null effect of SSRI exposure on autism rates in children.

    +
    curve1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "c", measure = "ratio", steps = 10000)
    +(ggcurve(data = curve1[[1]], type = "c", measure = "ratio", nullvalue = TRUE))
    +

    +
    curve2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "c", measure = "ratio", steps = 10000)
    +(ggcurve(data = curve2[[1]], type = "c", measure = "ratio", nullvalue = TRUE))
    +

    +

    The null value is shown via the red line and it’s clear that a large mass of the function is away from it.

    +

    We can also see this by plotting the likelihood functions via the curve_rev() function.

    +
    lik1 <- curve_rev(point = 1.7, LL = 1.1, UL = 2.6, type = "l", measure = "ratio", steps = 10000)
    +(ggcurve(data = lik1[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))
    +

    +
    lik2 <- curve_rev(point = 1.61, LL = 0.997, UL = 2.59,type = "l", measure = "ratio", steps = 10000)
    +(ggcurve(data = lik2[[1]], type = "l1", measure = "ratio", nullvalue = TRUE))
    +

    +

    We can also view the amount of agreement between the likelihood functions of these two studies.

    + +

    +

    and the consonance functions

    + +

    +
    +
    +

    +The Bootstrap and Consonance Functions

    +

    Some authors have shown that the bootstrap distribution is equal to the confidence distribution because it meets the definition of a consonance distribution.1,18,19 The bootstrap distribution and the asymptotic consonance distribution would be defined as:

    +

    \[H_{n}(\theta)=1-P\left(\hat{\theta}-\hat{\theta}^{*} \leq \hat{\theta}-\theta | \mathbf{x}\right)=P\left(\hat{\theta}^{*} \leq \theta | \mathbf{x}\right)\]

    +

    Certain bootstrap methods such as the BCa method and t-bootstrap method also yield second order accuracy of consonance distributions.

    +

    \[H_{n}(\theta)=1-P\left(\frac{\hat{\theta}^{*}-\hat{\theta}}{\widehat{S E}^{*}\left(\hat{\theta}^{*}\right)} \leq \frac{\hat{\theta}-\theta}{\widehat{S E}(\hat{\theta})} | \mathbf{x}\right)\]

    +

    Here, I demonstrate how to use these particular bootstrap methods to arrive at consonance curves and densities.

    +

    We’ll use the Iris dataset and construct a function that’ll yield a parameter of interest.

    + +

    We can now use the curve_boot() method to construct a function. The default method used for this function is the “BCa” method provided by the bcaboot package.19

    +

    I will suppress the output of the function because it is unnecessarily long. But we’ve placed all the estimates into a list object called y.

    +

    The first item in the list will be the consonance distribution constructed by typical means, while the third item will be the bootstrap approximation to the consonance distribution.

    +
    ggcurve(data = y[[1]])
    +

    +
    ggcurve(data = y[[3]])
    +

    +

    We can also print out a table for TeX documents

    +
    (gg <- curve_table(data = y[[1]], format = "image"))
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Lower Limit

    Upper Limit

    Interval Width

    Interval Level (%)

    CDF

    P-value

    S-value (bits)

    -0.142

    -0.093

    25

    0.048

    0.75

    0.415

    0.625

    -0.169

    -0.067

    50

    0.102

    0.50

    1.000

    0.750

    -0.205

    -0.031

    75

    0.174

    0.25

    2.000

    0.875

    -0.214

    -0.021

    80

    0.194

    0.20

    2.322

    0.900

    -0.266

    0.031

    95

    0.296

    0.05

    4.322

    0.975

    -0.312

    0.077

    99

    0.389

    0.01

    6.644

    0.995

    +

    More bootstrap replications will lead to a smoother function. But for now, we can compare these two functions to see how similar they are.

    +
    plot_compare(y[[1]], y[[3]])
    +

    +

    The densities can also be calculated accurately using the t-bootstrap method. Here we use a different dataset to show this

    +
    library(Lock5Data)
    +dataz <- data(CommuteAtlanta)
    +func = function(data, index) {
    +  x <- as.numeric(unlist(data[1]))
    +  y <- as.numeric(unlist(data[2]))
    +  return(mean(x[index]) - mean(y[index]))
    +}
    +

    Our function is a simple mean difference. This time, we’ll set the method to “t” for the t-bootstrap method

    +
    z <- curve_boot(data = CommuteAtlanta, func = func, method = "t", replicates = 2000, steps = 1000)
    +ggcurve(data = z[[1]])
    +

    +
    ggcurve(data = z[[2]], type = "cd")
    +

    +

    The consonance curve and density are nearly identical. With more bootstrap replications, they are very likely to converge.

    +
    (zz <- curve_table(data = z[[1]], format = "image"))
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Lower Limit

    Upper Limit

    Interval Width

    Interval Level (%)

    CDF

    P-value

    S-value (bits)

    -39.400

    -39.075

    0.325

    25.0

    0.625

    0.750

    0.415

    -39.611

    -38.876

    0.735

    50.0

    0.750

    0.500

    1.000

    -39.873

    -38.608

    1.265

    75.0

    0.875

    0.250

    2.000

    -39.932

    -38.530

    1.402

    80.0

    0.900

    0.200

    2.322

    -40.026

    -38.456

    1.570

    85.0

    0.925

    0.150

    2.737

    -40.118

    -38.354

    1.763

    90.0

    0.950

    0.100

    3.322

    -40.294

    -38.174

    2.120

    95.0

    0.975

    0.050

    4.322

    -40.442

    -38.026

    2.416

    97.5

    0.988

    0.025

    5.322

    -40.636

    -37.806

    2.830

    99.0

    0.995

    0.010

    6.644

    +
    +

    +Using Profile Likelihoods

    +

    For this last example, we’ll explore the curve_lik() function, which can help generate profile likelihood functions, and deviance statistics with the help of the ProfileLikelihood package.

    + +

    We’ll use a simple example taken directly from the ProfileLikelihood documentation where we’ll calculate the likelihoods from a glm model

    +
    data(dataglm)
    +xx <- profilelike.glm(y ~ x1 + x2, data=dataglm, profile.theta="group",
    +family=binomial(link="logit"), length=500, round=2)
    +#> Warning message: provide lo.theta and hi.theta
    +

    Then, we’ll use curve_lik() on the object that the ProfileLikelihood package created.

    + +

    Next, we’ll plot three functions, the relative likelihood, the log likelihood, the likelihoodm, and the deviance function.

    +
    ggcurve(lik[[1]], type = "l1")
    +

    +
    ggcurve(lik[[1]], type = "l2")
    +

    +
    ggcurve(lik[[1]], type = "l3")
    +

    +
    ggcurve(lik[[1]], type = "d")
    +

    +

    The obvious advantage of using reduced likelihoods is that they are free of nuisance parameters

    +

    \[L_{t_{n}}(\theta)=f_{n}\left(F_{n}^{-1}\left(H_{p i v}(\theta)\right)\right)\left|\frac{\partial}{\partial t} \psi\left(t_{n}, \theta\right)\right|=h_{p i v}(\theta)\left|\frac{\partial}{\partial t} \psi(t, \theta)\right| /\left.\left|\frac{\partial}{\partial \theta} \psi(t, \theta)\right|\right|_{t=t_{n}}\] thus, giving summaries of the data that can be incorporated into combined analyses.

    +
    +
    +
    +
    +

    +References

    +
    +
    +
    +

    1. Xie M-g, Singh K. Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review. International Statistical Review. 2013;81(1):3-39. doi:10.1111/insr.12000

    +
    +
    +

    2. Birnbaum A. A unified theory of estimation, I. The Annals of Mathematical Statistics. 1961;32(1):112-135. doi:10.1214/aoms/1177705145

    +
    +
    +

    3. Chow ZR, Greenland S. Semantic and Cognitive Tools to Aid Statistical Inference: Replace Confidence and Significance by Compatibility and Surprise. arXiv:190908579 [statME]. September 2019. http://arxiv.org/abs/1909.08579.

    +
    +
    +

    4. Fraser D. P-Values: The Insight to Modern Statistical Inference. Annual Review of Statistics and Its Application. 2017;4(1):1-14. doi:10.1146/annurev-statistics-060116-054139

    +
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    +

    5. Fraser DAS. The P-value function and statistical inference. The American Statistician. 2019;73(sup1):135-147. doi:10.1080/00031305.2018.1556735

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    +

    6. Poole C. Beyond the confidence interval. American Journal of Public Health. 1987;77(2):195-199. doi:10.2105/AJPH.77.2.195

    +
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    +

    7. Poole C. Confidence intervals exclude nothing. American Journal of Public Health. 1987;77(4):492-493. doi:10.2105/ajph.77.4.492

    +
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    8. Schweder T, Hjort NL. Confidence and Likelihood*. Scand J Stat. 2002;29(2):309-332. doi:10.1111/1467-9469.00285

    +
    +
    +

    9. Schweder T, Hjort NL. Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions. Cambridge University Press; 2016.

    +
    +
    +

    10. Singh K, Xie M, Strawderman WE. Confidence distribution (CD) – distribution estimator of a parameter. August 2007. http://arxiv.org/abs/0708.0976.

    +
    +
    +

    11. Sullivan KM, Foster DA. Use of the confidence interval function. Epidemiology. 1990;1(1):39-42. doi:10.1097/00001648-199001000-00009

    +
    +
    +

    12. Whitehead J. The case for frequentism in clinical trials. Statistics in Medicine. 1993;12(15-16):1405-1413. doi:10.1002/sim.4780121506

    +
    +
    +

    13. Rothman KJ, Greenland S, Lash TL. Precision and statistics in epidemiologic studies. In: Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd ed. Lippincott Williams & Wilkins; 2008:148-167.

    +
    +
    +

    14. Greenland S. Valid P-values behave exactly as they should: Some misleading criticisms of P-values and their resolution with S-values. The American Statistician. 2019;73(sup1):106-114. doi:10.1080/00031305.2018.1529625

    +
    +
    +

    15. Shannon CE. A mathematical theory of communication. The Bell System Technical Journal. 1948;27(3):379-423. doi:10.1002/j.1538-7305.1948.tb01338.x

    +
    +
    +

    16. Brown HK, Ray JG, Wilton AS, Lunsky Y, Gomes T, Vigod SN. Association between serotonergic antidepressant use during pregnancy and autism spectrum disorder in children. JAMA. 2017;317(15):1544-1552. doi:10.1001/jama.2017.3415

    +
    +
    +

    17. Brown HK, Hussain-Shamsy N, Lunsky Y, Dennis C-LE, Vigod SN. The association between antenatal exposure to selective serotonin reuptake inhibitors and autism: A systematic review and meta-analysis. The Journal of Clinical Psychiatry. 2017;78(1):e48-e58. doi:10.4088/JCP.15r10194

    +
    +
    +

    18. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. CRC Press; 1994.

    +
    +
    +

    19. Efron B, Narasimhan B. The automatic construction of bootstrap confidence intervals. October 2018:17.

    +
    +
    +
    +
    + + + +
    + + + + +
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  • - - -
    -

    -Dependencies

    +
    +

    +Dependencies

    • ggplot2
    • metafor
    • @@ -200,8 +180,8 @@


    -

    -Purpose

    +

    In particular, the usual 95% default forces the user’s focus onto parameter values that yield p > 0.05, without regard to the trivial difference between (say) p = 0.06 and p = 0.04 (a difference not even worth a coin toss). To address this problem, we first note that a 95% interval estimate is only one of a number of arbitrary dichotomization of possibilities of parameter values (into either inside or outside of an interval). A more accurate picture of uncertainty is then obtained by examining intervals using other percentiles, e.g., proportionally-spaced compatibility levels such as p 0.25, 0.05, 0.01, which correspond to 75%, 95%, 99% CIs and equally-spaced S-values of s < 2, 4.32, 6.64 bits. When a detailed picture is desired, a table or graph of all the P-values and S-values across a broad range of parameter values seems the clearest way to see how compatibility varies smoothly across the values.

    Graphs of P-values or their equivalent have been promoted for decades [34, 56–59], yet their adoption has been slight. Nonetheless, P-value and S-value graphing software is now available freely through several statistical packages [60–62]. A graph of the P-values p against possible parameter values allows one to see at a glance which parameter values are most compatible with the data under the background assumptions. This graph is known as the P-value function, or compatibility, consonance, or confidence curve [34, 57, 58, 63, 64]. Transforming the corresponding P-values in the graph to S-values produces an S-value (surprisal) function.

    @@ -242,23 +222,23 @@

    Session info

    - +

    diff --git a/pkgdown/extra.css b/pkgdown/extra.css index 1634f08..e02d945 100644 --- a/pkgdown/extra.css +++ b/pkgdown/extra.css @@ -46,7 +46,8 @@ a:focus { } h1, .h1 { - font-size: 32px; + font-size: 25px; + font-weight: 900; } h1 small, .h1 small { @@ -56,15 +57,15 @@ h1 small, .h1 small { } h2, .h2 { - font-size: 28px; + font-size: 25px; } h3, .h3 { - font-size: 20px; + font-size: 24px; } h4, .h4 { - font-size: 20px; + font-size: 23px; } .contents h1, .contents h2, .contents h3, .contents h4 { diff --git a/vignettes/examples.Rmd b/vignettes/examples.Rmd index daa78eb..30e17b9 100644 --- a/vignettes/examples.Rmd +++ b/vignettes/examples.Rmd @@ -93,8 +93,8 @@ First, we generate some more fake data GroupA2 <- rnorm(500) GroupB2 <- rnorm(500) RandomData2 <- data.frame(GroupA2, GroupB2) -model <- glm(GroupA2 ~ GroupB2, data = RandomData2) -randomframe <- curve_gen(model, "GroupB2", method = "glm", steps = 10000) +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) +randomframe <- curve_gen(model, "GroupB2") ``` Once again, we'll plot this data with `ggcurve()`. We can also indicate whether we want certain interval estimates to be plotted in the function with the "levels" argument. If we wanted to plot the 50%, 75%, and 95% intervals, we'd provide the argument this way:

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    -
    - - - - -
    - -
    -
    - - -
    -

    Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages -to generate consonance distrbutions.

    -
    - -
    curve_boot(
    -  data = data,
    -  func = func,
    -  method = "bca",
    -  replicates = 2000,
    -  steps = 1000,
    -  table = TRUE
    -)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    data

    Dataset that is being used to create a consonance function.

    func

    Custom function that is used to create parameters of interest that -will be bootstrapped.

    method

    The boostrap method that will be used to generate the functions. -Methods include "bca" which is the default and "t".

    replicates

    Indicates how many bootstrap replicates are to be performed. -The defaultis currently 20000 but more may be desirable, especially to make -the functions more smooth.

    steps

    Indicates how many consonance intervals are to be calculated at -various levels. For example, setting this to 100 will produce 100 consonance -intervals from 0 to 100. Setting this to 10000 will produce more consonance -levels. By default, it is set to 1000. Increasing the number substantially -is not recommended as it will take longer to produce all the intervals and -store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant -statistics should be generated. The default is TRUE and generates a table -which is included in the list object.

    - -

    Value

    - -

    A list with the dataframe of values in the first list and the table -in the second if table = TRUE.

    - -

    Examples

    -
    # \donttest{ -data(diabetes, package = "bcaboot") -Xy <- cbind(diabetes$x, diabetes$y) -rfun <- function(Xy) { - y <- Xy[, 11] - X <- Xy[, 1:10] - return(summary(lm(y ~ X))$adj.r.squared) -} - -x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000)
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    -ggconcurve(data = x[[1]])
    ggconcurve(data = x[[3]])
    -plot_compare(x[[1]], x[[3]])
    # } - -
    -
    - -
    - - - -
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    +
    + + + + +
    + +
    +
    + + +
    +

    Use the BCa bootstrap method and the t boostrap method from the bcaboot and boot packages +to generate consonance distrbutions.

    +
    + +
    curve_boot(
    +  data = data,
    +  func = func,
    +  method = "bca",
    +  replicates = 2000,
    +  steps = 1000,
    +  table = TRUE
    +)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    data

    Dataset that is being used to create a consonance function.

    func

    Custom function that is used to create parameters of interest that +will be bootstrapped.

    method

    The boostrap method that will be used to generate the functions. +Methods include "bca" which is the default and "t".

    replicates

    Indicates how many bootstrap replicates are to be performed. +The defaultis currently 20000 but more may be desirable, especially to make +the functions more smooth.

    steps

    Indicates how many consonance intervals are to be calculated at +various levels. For example, setting this to 100 will produce 100 consonance +intervals from 0 to 100. Setting this to 10000 will produce more consonance +levels. By default, it is set to 1000. Increasing the number substantially +is not recommended as it will take longer to produce all the intervals and +store them into a dataframe.

    table

    Indicates whether or not a table output with some relevant +statistics should be generated. The default is TRUE and generates a table +which is included in the list object.

    + +

    Value

    + +

    A list with the dataframe of values in the first list and the table +in the second if table = TRUE.

    + +

    Examples

    +
    # \donttest{ +data(diabetes, package = "bcaboot") +Xy <- cbind(diabetes$x, diabetes$y) +rfun <- function(Xy) { + y <- Xy[, 11] + X <- Xy[, 1:10] + return(summary(lm(y ~ X))$adj.r.squared) +} + +x <- curve_boot(data = Xy, func = rfun, method = "bca", replicates = 2000, steps = 1000)
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    +ggconcurve(data = x[[1]])
    ggconcurve(data = x[[3]])
    +plot_compare(x[[1]], x[[3]])
    # } + +
    +
    + +
    + + + +
  • +
    + + + + +
    + +
    +
    + + +
    +

    +
    + +
    plot_compare(data1, data2, type = "consonance", measure = "default", nullvalue = "absent", position = "pyramid", title = "Interval Function", subtitle = "The function displays intervals at every level.", xaxis = "Θ Range of Values", yaxis = "P-value", color = "#555555", fill1 = "#239a98", fill2 = "#d46c5b")
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    data1

    data2

    type

    measure

    nullvalue

    position

    title

    subtitle

    xaxis

    yaxis

    color

    fill1

    fill2

    + +

    Details

    + +

    +

    Value

    + +

    + + + +

    +

    References

    + +

    +

    Note

    + +

    +

    See also

    + +

    + +

    Examples

    +
    library(concurve) + +GroupA <- rnorm(500) +GroupB <- rnorm(500) + +RandomData <- data.frame(GroupA, GroupB) + + +intervalsdf <- curve_mean(GroupA, GroupB, + data = RandomData, method = "default" +) + + +(function1 <- ggconcurve(type = "consonance", intervalsdf, xaxis = ("\u0398 Range of Values")))
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    +GroupA2 <- rnorm(500) +GroupB2 <- rnorm(500) + +RandomData2 <- data.frame(GroupA2, GroupB2) + +model <- lm(GroupA2 ~ GroupB2, data = RandomData2) + +randomframe <- curve_gen(model, "GroupB2") + +(function2 <- ggconcurve(type = "consonance", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every level."))
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    +(function3 <- ggconcurve(type = "surprisal", randomframe, levels = c(0.25, 0.50, 0.75, 0.85, 0.90, 0.95), nullvalue = "present", subtitle = "The function displays compatibility intervals at every surprisal level."))
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    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "consonance", measure = "default", nullvalue = "present", title = "Comparison of Consonance Functions", subtitle = "The functions display compatibility intervals at every level."))
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    +(plot_compare(data1 = intervalsdf, data2 = randomframe, type = "surprisal", measure = "default", nullvalue = "present", title = "Comparison of Surprisal Functions", subtitle = "The functions display compatibility intervals at every surprisal level."))
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    +lik1 <- curve_lik(point = 1.7, LL = 1.1, UL = 2.6, measure = "ratio", steps = 10000) +(ggconcurve(data = lik1, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "The function displays likelihood intervals at every level."))
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    +lik2 <- curve_lik(point = 1.61, LL = 0.997, UL = 2.59, measure = "ratio", steps = 10000) +(ggconcurve(data = lik2, type = "deviance", measure = "ratio", nullvalue = "present", +subtitle = "The function displays deviance intervals at every level."))
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    + +(plot_compare(data1 = lik1, data2 = lik2, type = "likelihood", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Likelihood Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio"))
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    +(plot_compare(data1 = lik1, data2 = lik2, type = "deviance", measure = "ratio", nullvalue = "present", subtitle = "Comparison of Deviance Functions", xaxis = "\u0398 = Hazard Ratio / Odds Ratio"))
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    + +
    +
    + +
    + + + +
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