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Merge pull request #121 from egouldo/118-log-transform-yi
118 log transform yi
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| Package: ManyEcoEvo | ||
| Title: Meta-analyse data from 'Many-Analysts' style studies | ||
| Version: 2.4.2 | ||
| Version: 2.7.0 | ||
| Authors@R: c( | ||
| person("Elliot", "Gould", , "[email protected]", role = c("aut", "cre"), | ||
| comment = c(ORCID = "https://orcid.org/0000-0002-6585-538X")), | ||
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| # ManyEcoEvo 2.4.2 | ||
| # ManyEcoEvo (development version) | ||
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| # ManyEcoEvo 2.4.1 | ||
| <!-- NEWS.md is maintained by https://cynkra.github.io/fledge, do not edit --> | ||
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| * Initial CRAN submission. | ||
| - #118 docs: Add explanation about updated behaviour when `estimate_type` is missing in `ManyEcoEvo` dataframe | ||
| - #118 build: devtools::document() | ||
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| # ManyEcoEvo 2.6.0 | ||
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| - Update arg supply to targets call on prepare_response_variables() after #118 updates | ||
| - #118 add pmap internal helper function for differential application of transformation / standardisation in `standardise_response()` | ||
| - #118 delete old pmap helper function | ||
| - `dat` to `data` to help with auto-matching in pmap within prepare_response_variables() wrapper #118 | ||
| - ensure all family fns have ... arg for pmap application in prepare_response_variables() since all fns have different argument lengths and names | ||
| - accidentally deleted when upgrading for #118, have added creation of transform_datasets tibbles for all cases now, and then these will apply the appropriate functions in final code chunk at end | ||
| - #118 ensure application of Z_VZ_preds takes the generalised colnames yi, yi_se instead of using hard-coded dataset application #97 | ||
| - #118 call new arg `dataset_log_transform` in fn to log-transform outcomes for euc yi analysis | ||
| - #118 add log-transformation equivalent to `standardise_response()` and `process_resonse()` | ||
| - #102 add function documentation, including examples | ||
| - #118 extract `lower` and `upper` transformed vals in line with addition of `log_transform_response()` / changes to `standardise_response()` | ||
| - #116 check appropriate required variable (i.e. function needs `back_transformed_data`, but checked for `augmented_data` in `dat` arg, wouldn't throw required error because `augmented_data` was present in `dat` | ||
| - #102 add import, return, and see also roxygen doc tags, replace note with details tag, rename fn doc title | ||
| - #116 update argument checks conditional expression | ||
| - #118 match output to `log_transform_yi()` (now returns additional cols `lower` and `upper`, not only `c("Z","VZ")`) | ||
| - #118 match process to `log_transform_yi()` and #97 generalise processing to both euc/bt datasets without hard-coding dataset names in fns, and remove associated dataset-specific argument checking #116 | ||
| - #118 adapt response variable preparation to accept additional argument `dataset_log_transform` apply argument checks #116, add roxygen param #102 | ||
| - #118 adapt response variable processing to accept either/or/none for dataset standardisation/log-transformation. | ||
| - equivalent to `pred_to_z()` | ||
| - #102 write documentation | ||
| - #102 add import tags for `log_transform()` and link to equivalent functions, apply default argument values / checks | ||
| - #97 rename out argument |
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| #' Apply VZ exclusion to a data-frame containing list-columns of yi subsets | ||
| #' | ||
| #' @param df A dataframe of yi data subsets generated from `generate_yi_subsets\(\)`. | ||
| #' @param VZ_cutoff A numeric vector of length 1, values equal to or greater than this value of VZ will be filtered out of the dataframes stored in `df`'s list-column `data`. | ||
| #' | ||
| #' @param df A dataframe of yi data subsets generated by [generate_yi_subsets()] or [split_yi_subsets()]. | ||
| #' @param VZ_cutoff A numeric vector of length 1, values equal to or greater than this value of VZ will be filtered out of the dataframes stored in `df`'s list-column `data`, else a named list of numeric values, where the names are the dataset names and the values are the `VZ_cutoff`s for each `dataset` in `df`. | ||
| #' @param VZ_colname Either A character vector of length 1, the name of the column in the dataframes stored in `df`'s list-column `data` that contains the VZ values. Or else a named list of character values, where the names are the dataset names and the values are the `VZ_colname`s for each `dataset` in `df`. | ||
| #' @return A dataframe of yi subsets, whose extreme values of VZ have been removed. | ||
| #' @export | ||
| #' @import dplyr | ||
| #' @importFrom purrr map map2 | ||
| #' @importFrom pointblank col_exists | ||
| #' @importFrom cli cli_alert_warning | ||
| #' @seealso Applies [exclude_extreme_VZ()] to each dataframe in the list-column `data` of `df`. | ||
| #' @family Multi-dataset Wrapper Functions | ||
| apply_VZ_exclusions <- function(df = data.frame(), VZ_cutoff = numeric(1L)) { | ||
| pointblank::col_exists(df, columns = c("data", "diversity_data")) | ||
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| #' @details | ||
| #' `df` must contain the columns `"data"`, `"diversity_data"` and `"dataset"`. | ||
| #' If only one value of `VZ_colname` and `VZ_cutoff` is supplied, it will be recycled to match the number of datasets in `df`. | ||
| #' | ||
| #' If a named list is supplied for `VZ_colname` and `VZ_cutoff`, the names must match the dataset names in `df`. | ||
| #' @examples | ||
| #' data(ManyEcoEvo_yi) | ||
| #' ManyEcoEvo_yi %>% | ||
| #' prepare_response_variables( | ||
| #' estimate_type = "yi", | ||
| #' param_table = | ||
| #' ManyEcoEvo:::analysis_data_param_tables, | ||
| #' dataset_standardise = "blue tit", | ||
| #' dataset_log_transform = "eucalyptus") %>% | ||
| #' generate_yi_subsets() %>% | ||
| #' apply_VZ_exclusions(VZ_colname = | ||
| #' list("eucalyptus" = "se_log", | ||
| #' "blue tit" = "VZ"), | ||
| #' VZ_cutoff = 3) | ||
| apply_VZ_exclusions <- function(df = data.frame(), VZ_colname, VZ_cutoff) { | ||
| # ---- Argument Checking ----- | ||
| pointblank::col_exists(df, columns = c("data", "diversity_data", "dataset")) | ||
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| if (!is.null(names(VZ_colname))) { | ||
| pointblank::expect_col_vals_make_set(object = df, | ||
| columns = dataset, | ||
| set = names(VZ_colname)) | ||
| } | ||
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| if (!is.null(names(VZ_cutoff))) { | ||
| pointblank::expect_col_vals_make_set(object = df, | ||
| columns = dataset, | ||
| set = names(VZ_cutoff)) | ||
| } | ||
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| cli::cli_h1("Applying VZ exclusions") | ||
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| if (is.list(VZ_colname)) { | ||
| map(VZ_colname, ~ { | ||
| stopifnot( | ||
| is.character(.x) | ||
| ) | ||
| }) | ||
| } else { | ||
| stopifnot( | ||
| is.character(VZ_colname) | ||
| ) | ||
| # ----- Format VZ exclusions when VZ_colname is not list ----- | ||
| if (length(VZ_colname) < length(unique(df$dataset))) { | ||
| cli::cli_alert_warning("{.arg VZ_colname} = {.val {VZ_cutoff}} was recycled to match the number of unique datasets in {.arg df}.") | ||
| VZ_colname <- rep(VZ_colname, length(unique(df$dataset))) | ||
| } | ||
| } | ||
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| if (is.list(VZ_cutoff)) { | ||
| map(VZ_cutoff, ~ { | ||
| stopifnot( | ||
| is.numeric(.x) | ||
| ) | ||
| }) | ||
| } else { | ||
| stopifnot( | ||
| is.numeric(VZ_cutoff) | ||
| ) | ||
| # ----- Format VZ exclusions when VZ_cutoff is not list ----- | ||
| if (length(VZ_cutoff) < length(unique(df$dataset))) { | ||
| cli::cli_alert_warning("{.arg VZ_cutoff} = {.val {VZ_cutoff}} was recycled to match the number of unique datasets in {.arg df}.") | ||
| VZ_cutoff <- rep(VZ_cutoff, length(unique(df$dataset))) | ||
| } | ||
| } | ||
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| # ----- Create formulas for matching VZ_cutoff and VZ_colname to df$dataset ----- | ||
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| if (is.list(VZ_colname)) { | ||
| formulae_match_VZ_colname <- map2(names(VZ_colname), | ||
| VZ_colname, | ||
| rlang::new_formula) | ||
| } else { | ||
| formulae_match_VZ_colname <- map2(unique(df$dataset), | ||
| VZ_colname, | ||
| rlang::new_formula) | ||
| } | ||
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| if (is.list(VZ_cutoff)) { | ||
| formulae_match_VZ_cutoff <- map2(names(VZ_cutoff), | ||
| VZ_cutoff, | ||
| rlang::new_formula) | ||
| } else { | ||
| formulae_match_VZ_cutoff <- map2(unique(df$dataset), | ||
| VZ_cutoff, | ||
| rlang::new_formula) | ||
| } | ||
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| # ----- Apply VZ exclusions ----- | ||
| df_out <- df %>% | ||
| ungroup() %>% | ||
| mutate( | ||
| data = map(data, exclude_extreme_VZ, !!{{ VZ_cutoff }}), # TODO check whether we should run on effects_analysis instead of data | ||
| VZ_colname_val = case_match(dataset, | ||
| !!!formulae_match_VZ_colname, | ||
| .default = NA), | ||
| VZ_cutoff_val = case_match(dataset, | ||
| !!!formulae_match_VZ_cutoff, | ||
| .default = NA), | ||
| data = pmap( # TODO check whether we should run on effects_analysis instead of data | ||
| list(data, VZ_colname_val, VZ_cutoff_val), | ||
| ~ exclude_extreme_VZ(df = ..1, VZ_colname = ..2, VZ_cutoff = ..3) | ||
| ), | ||
| diversity_data = map2( | ||
| .x = diversity_data, | ||
| .y = data, | ||
| .f = ~ semi_join(.x, .y, by = "id_col") | ||
| ) | ||
| ) | ||
| ) %>% | ||
| select(-VZ_colname_val, -VZ_cutoff_val) | ||
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| return(df_out) | ||
| } |
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