Merge pull request #55 from sinhrks/v0042

Fix for CRAN

DESCRIPTION

@@ -2,7 +2,7 @@ Package: ggfortify
 Type: Package
 Title: Data Visualization Tools for Statistical Analysis Results
 Version: 0.0.4
-Date: 2015-09-20
+Date: 2015-10-03
 Author: Masaaki Horikoshi, Yuan Tang
 Maintainer: Masaaki Horikoshi <[email protected]>
 Description: Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.
@@ -17,15 +17,18 @@ Suggests:
     dlm,
     fGarch,
     forecast,
+    grDevices,
     KFAS,
     knitr,
     MASS,
     MSwM,
     nlme,
+    stats,
     strucchange,
     survival,
     timeSeries,
     tseries,
+    utils,
     vars,
     xts,
     zoo,

R/base_fortify_ts.R

@@ -259,13 +259,13 @@ fortify.tsmodel <- function(model, data = NULL,
   if (is(model, 'Arima') || is(model, 'ar')) {
     if (is.null(data)) {
       data <- forecast::getResponse(model)
-      fit <- fitted(model)
+      fit <- stats::fitted(model)
     } else {
-      fit <- data - residuals(model)
+      fit <- data - stats::residuals(model)
     }
     d <- ggplot2::fortify(data, is.date = is.date)
     fit <- ggplot2::fortify(fit, data.name = 'Fitted', is.date = is.date)
-    resid <- ggplot2::fortify(residuals(model), data.name = 'Residuals', is.date = is.date)
+    resid <- ggplot2::fortify(stats::residuals(model), data.name = 'Residuals', is.date = is.date)
 
     if (!is.null(predict)) {
       pred <- ggplot2::fortify(predict$pred, data.name = 'Predicted')
@@ -276,8 +276,8 @@ fortify.tsmodel <- function(model, data = NULL,
   } else if (is(model, 'HoltWinters')) {
     # same as fracdiff and nnetar
     d <- ggplot2::fortify(model$x, is.date = is.date)
-    fit <- ggplot2::fortify(fitted(model), data.name = 'Fitted', is.date = is.date)
-    resid <- ggplot2::fortify(residuals(model), data.name = 'Residuals', is.date = is.date)
+    fit <- ggplot2::fortify(stats::fitted(model), data.name = 'Fitted', is.date = is.date)
+    resid <- ggplot2::fortify(stats::residuals(model), data.name = 'Residuals', is.date = is.date)
 
     if (!is.null(predict)) {
       pred <- ggplot2::fortify(predict)
@@ -289,8 +289,8 @@ fortify.tsmodel <- function(model, data = NULL,
     }
   } else if (is(model, 'fracdiff') || is(model, 'nnetar')) {
     d <- ggplot2::fortify(model$x, is.date = is.date)
-    fit <- ggplot2::fortify(fitted(model), data.name = 'Fitted', is.date = is.date)
-    resid <- ggplot2::fortify(residuals(model), data.name = 'Residuals', is.date = is.date)
+    fit <- ggplot2::fortify(stats::fitted(model), data.name = 'Fitted', is.date = is.date)
+    resid <- ggplot2::fortify(stats::residuals(model), data.name = 'Residuals', is.date = is.date)
   } else if (is(model, 'fGARCH')) {
     index <- attr(model@data, 'names')
     index <- as.vector(index)
@@ -402,14 +402,6 @@ fortify.KFS <- fortify.tsmodel
 #' autoplot(d.holt)
 #' autoplot(d.holt, predict = predict(d.holt, n.ahead = 5))
 #' autoplot(d.holt, predict = predict(d.holt, n.ahead = 5, prediction.interval = TRUE))
-#'
-#' form <- function(theta){
-#'   dlm::dlmModPoly(order=1, dV=exp(theta[1]), dW=exp(theta[2]))
-#' }
-#' model <- form(dlm::dlmMLE(Nile, parm=c(1, 1), form)$par)
-#' filtered <- dlm::dlmFilter(Nile, model)
-#' autoplot(filtered)
-#' autoplot(dlm::dlmSmooth(filtered))
 #' @export
 autoplot.tsmodel <- function(object, data = NULL,
                              predict = NULL,

R/fortify_stats.R

@@ -366,18 +366,18 @@ autoplot.pca_common <- function(object, data = NULL,
   if (frame) {
     if (frame.type == 'convex') {
       if (is.null(frame.colour) || !(frame.colour %in% colnames(plot.data))) {
-        hulls <- plot.data[chull(plot.data[c(x.column, y.column)]), ]
+        hulls <- plot.data[grDevices::chull(plot.data[c(x.column, y.column)]), ]
       } else {
         hulls <- plot.data %>%
           dplyr::group_by_(frame.colour) %>%
-          dplyr::do(.[chull(.[c(x.column, y.column)]), ])
+          dplyr::do(.[grDevices::chull(.[c(x.column, y.column)]), ])
       }
       mapping = aes_string(colour = frame.colour, fill = frame.colour)
       p <- p + ggplot2::geom_polygon(data = hulls, mapping = mapping,
                                      alpha = frame.alpha)
     } else if (frame.type %in% c('t', 'norm', 'euclid')) {
       ggversion <- utils::packageVersion('ggplot2')
-      if (compareVersion(as.character(ggversion), '1.0.0') >= 0) {
+      if (utils::compareVersion(as.character(ggversion), '1.0.0') >= 0) {
         mapping = aes_string(colur = frame.colour, fill = frame.colour)
         p <- p + ggplot2::stat_ellipse(mapping = mapping,
                                        level = frame.level, type = frame.type,
@@ -435,10 +435,10 @@ autoplot.dist <- autoplot.matrix
 #' fortify(stepfun(c(1, 2, 3, 4, 5, 6, 7, 8, 10), c(4, 5, 6, 7, 8, 9, 10, 11, 12, 9)))
 #' @export
 fortify.stepfun <- function(model, data, ...) {
-  x <- knots(model)
+  x <- stats::knots(model)
   lim <- range(x)
   if (length(x) > 1L) {
-    dr <- max(0.08 * diff(lim), median(diff(x)))
+    dr <- max(0.08 * diff(lim), stats::median(diff(x)))
   } else {
     dr <- abs(x) / 16
   }

R/fortify_stats_lm.R

@@ -80,11 +80,11 @@ autoplot.lm <- function(object, which = c(1:3, 5), data = NULL,
   } else if (is_glm) {
     sqrt(summary(object)$dispersion)
   } else {
-    sqrt(deviance(object)/stats::df.residual(object))
+    sqrt(stats::deviance(object)/stats::df.residual(object))
   }
   label.fitted <- ifelse(is_glm, 'Predicted values', 'Fitted values')
   label.y23 <- ifelse(is_glm, 'Std. deviance resid.', 'Standardized residuals')
-  hii <- lm.influence(object, do.coef = FALSE)$hat
+  hii <- stats::lm.influence(object, do.coef = FALSE)$hat
 
   if (is.logical(shape) && !shape) {
     if (missing(label)) {
@@ -98,9 +98,9 @@ autoplot.lm <- function(object, which = c(1:3, 5), data = NULL,
 
   if (label.n > 0L) {
     r.data <- dplyr::arrange_(plot.data, 'dplyr::desc(abs(.resid))')
-    r.data <- head(r.data, label.n)
+    r.data <- utils::head(r.data, label.n)
     cd.data <- dplyr::arrange_(plot.data, 'dplyr::desc(abs(.cooksd))')
-    cd.data <- head(cd.data, label.n)
+    cd.data <- utils::head(cd.data, label.n)
   }
 
   .smooth <- function(x, y) {
@@ -147,9 +147,9 @@ autoplot.lm <- function(object, which = c(1:3, 5), data = NULL,
   if (show[2L]) {
     t2 <- 'Normal Q-Q'
     qprobs <- c(0.25, 0.75)
-    qy <- quantile(plot.data$.stdresid, probs = qprobs, names = FALSE,
-                   type = 7, na.rm = TRUE)
-    qx <- qnorm(qprobs)
+    qy <- stats::quantile(plot.data$.stdresid, probs = qprobs, names = FALSE,
+                          type = 7, na.rm = TRUE)
+    qx <- stats::qnorm(qprobs)
     slope <- diff(qy) / diff(qx)
     int <- qy[1L] - slope * qx[1L]
 
@@ -241,7 +241,7 @@ autoplot.lm <- function(object, which = c(1:3, 5), data = NULL,
                          title = t6)
 
     g <- dropInf(hii/(1 - hii), hii)
-    p <- length(coef(object))
+    p <- length(stats::coef(object))
     bval <- pretty(sqrt(p * plot.data$.cooksd / g), 5)
     for (i in seq_along(bval)) {
       bi2 <- bval[i]^2

R/tslib.R

@@ -134,7 +134,7 @@ confint.acf <- function (x, ci = 0.95, ci.type = "white") {
     with.ci.ma <- FALSE
   }
   clim0 <- if (with.ci)
-    qnorm((1 + ci)/2)/sqrt(x$n.used)
+    stats::qnorm((1 + ci)/2)/sqrt(x$n.used)
   else c(0, 0)
 
   Npgs <- 1L
@@ -182,7 +182,7 @@ confint.acf <- function (x, ci = 0.95, ci.type = "white") {
 fitted.ar <- function(object, ...) {
   requireNamespace('forecast')
   x <- forecast::getResponse(object)
-  return(x - residuals(object))
+  return(x - stats::residuals(object))
 }
 
 #' Calcurate residuals for \code{stats::ar}
@@ -217,18 +217,18 @@ ggcpgram <- function (ts, taper = 0.1,
 
   x <- as.vector(ts)
   x <- x[!is.na(x)]
-  x <- spec.taper(scale(x, TRUE, FALSE), p = taper)
-  y <- Mod(fft(x)) ^ 2 / length(x)
+  x <- stats::spec.taper(scale(x, TRUE, FALSE), p = taper)
+  y <- Mod(stats::fft(x)) ^ 2 / length(x)
   y[1L] <- 0
   n <- length(x)
-  x <- (0:(n / 2)) * frequency(ts) / n
+  x <- (0:(n / 2)) * stats::frequency(ts) / n
   if (length(x)%%2 == 0) {
     n <- length(x) - 1
     y <- y[1L:n]
     x <- x[1L:n]
   }
   else y <- y[seq_along(x)]
-  xm <- frequency(ts) / 2
+  xm <- stats::frequency(ts) / 2
   mp <- length(x) - 1
   crit <- 1.358 / (sqrt(mp) + 0.12 + 0.11 / sqrt(mp))
 
@@ -269,7 +269,7 @@ ggtsdiag <- function(object, gof.lag = 10,
                      conf.int.fill = NULL, conf.int.alpha = 0.3,
                      ad.colour = '#888888', ad.linetype = 'dashed', ad.size = .2,
                      nrow = NULL, ncol = 1, ...) {
-  rs <- residuals(object)
+  rs <- stats::residuals(object)
   if (is.null(rs)) {
     rs <- object$residuals
   }
@@ -284,7 +284,7 @@ ggtsdiag <- function(object, gof.lag = 10,
                         colour = ad.colour) +
     ggplot2::ggtitle('Standardized Residuals')
 
-  acfobj <- stats::acf(rs, plot = FALSE, na.action = na.pass)
+  acfobj <- stats::acf(rs, plot = FALSE, na.action = stats::na.pass)
   p.acf <- autoplot.acf(acfobj, conf.int = conf.int,
                         conf.int.colour = conf.int.colour,
                         conf.int.linetype = conf.int.linetype,
@@ -294,7 +294,7 @@ ggtsdiag <- function(object, gof.lag = 10,
 
   nlag <- gof.lag
   pval <- numeric(nlag)
-  for (i in 1L:nlag) pval[i] <- Box.test(rs, i, type = "Ljung-Box")$p.value
+  for (i in 1L:nlag) pval[i] <- stats::Box.test(rs, i, type = "Ljung-Box")$p.value
   lb.df <- data.frame(Lag = 1L:nlag, `p value` = pval,
                       lower = -0.05, upper = 0.05)
   # Unnable to create column with space by above expression
@@ -379,7 +379,7 @@ ggfreqplot <- function(data, freq = NULL,
   is.univariate(data)
 
   if (is.null(freq)) {
-    freq <- frequency(data)
+    freq <- stats::frequency(data)
   }
 
   if (is.null(nrow) && is.null(ncol)) {
@@ -401,8 +401,8 @@ ggfreqplot <- function(data, freq = NULL,
     dplyr::summarise_(m = 'mean(Data)', s = 'sd(Data)')
 
   p <- (1 - conf.int.value) / 2
-  summarised$lower <- qnorm(p, mean = summarised$m, sd = summarised$s)
-  summarised$upper <- qnorm(1 - p, mean = summarised$m, sd = summarised$s)
+  summarised$lower <- stats::qnorm(p, mean = summarised$m, sd = summarised$s)
+  summarised$upper <- stats::qnorm(1 - p, mean = summarised$m, sd = summarised$s)
 
   d <- dplyr::left_join(d, summarised, by = 'Frequency')
 

R/util.R

@@ -63,14 +63,6 @@ deprecate.warning <- function(old.kw, new.kw) {
   warning(message, call. = FALSE)
 }
 
-#' Raise error for unsupported type
-#'
-#' @return NULL
-stop.unsupported.type <- function() {
-  stop(paste0('Unsupported class for autoplot: ', class(data)), call. = FALSE)
-}
-
-
 #' Post process for fortify.
 #'
 #' @param data data.frame

man/autoplot.tsmodel.Rd

@@ -90,13 +90,5 @@ d.holt <- stats::HoltWinters(USAccDeaths)
 autoplot(d.holt)
 autoplot(d.holt, predict = predict(d.holt, n.ahead = 5))
 autoplot(d.holt, predict = predict(d.holt, n.ahead = 5, prediction.interval = TRUE))
-
-form <- function(theta){
-  dlm::dlmModPoly(order=1, dV=exp(theta[1]), dW=exp(theta[2]))
-}
-model <- form(dlm::dlmMLE(Nile, parm=c(1, 1), form)$par)
-filtered <- dlm::dlmFilter(Nile, model)
-autoplot(filtered)
-autoplot(dlm::dlmSmooth(filtered))
 }
 

tests/testthat/test-base-infer.R

@@ -65,26 +65,26 @@ test_that('infer, fortify and autoplot works for dlm::dlmSmooth', {
 
   autoplot(filtered)
   autoplot(smoothed)
-  
+
   form <- function(parm){
     dlmModPoly(order = 2, dV = exp(parm[1]), dW = exp(parm[2:3]))
   }
   y <- log(Nile)
   model <- form(dlm::dlmMLE(y, parm=log(c(var(y), 0.001, 0.001)), form)$par)
   parm <- log(c(var(y), 0.001, 0.001))
-  
+
   filtered <- dlm::dlmFilter(y, model)
   fortified <- fortify(filtered)
   expect_equal(colnames(fortified), c('Index', 'Data', 'Fitted', 'Residuals'))
   expect_equal(fortified$Index, nile_fortified$Index)
-  
+
   smoothed <- dlm::dlmSmooth(filtered)
   expect_equal(infer(smoothed), 'dlmSmooth')
-  
+
   fortified <- fortify(smoothed)
   expect_equal(colnames(fortified), c('Index', 'Data'))
   expect_equal(fortified$Index, nile_fortified$Index)
-  
+
   autoplot(filtered)
   autoplot(smoothed)
 })