remove reverse_item

Author: strengejacke

NEWS.md

@@ -23,10 +23,6 @@
 * New function `factor_scores()` to extract factor scores from EFA (`psych::fa()`
   or `factor_analysis()`).
 
-* `get_scores()` gets a `reverse_items` argument, to reverse the scale of
-  items before calculating the sum scores of subscales. This is useful
-  when the items are not coded in the same direction.
-
 * Added and/or improved print-methods for all functions around PCA, FA and Omega.
 
 * Improved efficiency in `model_parameters()` for models from packages *brms*

R/factor_analysis.R

@@ -14,30 +14,10 @@ factor_analysis.data.frame <- function(x,
                                        sort = FALSE,
                                        threshold = NULL,
                                        standardize = FALSE,
-                                       reverse_items = NULL,
                                        verbose = TRUE,
                                        ...) {
   insight::check_if_installed("psych")
 
-  # should some items be reversed?
-  if (!is.null(reverse_items)) {
-    # only works for data frames, not matrices
-    if (!is.data.frame(x)) {
-      insight::format_error(
-        "The `reverse_items` argument only works with data frames, not matrices."
-      )
-    }
-    # numeric indices should be replaced by their column names
-    if (is.numeric(reverse_items)) {
-      reverse_items <- colnames(x)[reverse_items]
-    }
-    if (verbose) {
-      insight::format_alert(paste("Reversing items:", toString(reverse_items)))
-    }
-    # reverse the items
-    x <- datawizard::reverse_scale(x, reverse_items, verbose = verbose)
-  }
-
   # Standardize
   if (standardize) {
     x <- datawizard::standardize(x, ...)
@@ -60,7 +40,6 @@ factor_analysis.data.frame <- function(x,
   )
 
   attr(out, "dataset") <- x
-  attr(out, "reverse_items") <- reverse_items
   out
 }
 

R/principal_components.R

@@ -53,12 +53,6 @@
 #'   computes a correlation matrix and uses that r-matrix for the factor analysis
 #'   by default - therefore, standardization of the raw variables is unnecessary,
 #'   and even undesirable when using `cor = "poly"`).
-#' @param reverse_items Character vector of variable names or numeric indices
-#'   indicating their column positions in `x` that should be reversed before
-#'   computing the PCA or FA. This is useful when the items are not coded in the
-#'   same direction. If `NULL` (default), no items are reversed. **Note:** The
-#'   data frame that is stored as attribute in the returned object is the modified
-#'   version of `x`, with the reversed items.
 #' @param object An object of class `parameters_pca`, `parameters_efa` or
 #'   `psych_efa`.
 #' @param newdata An optional data frame in which to look for variables with
@@ -295,7 +289,6 @@ principal_components.data.frame <- function(x,
                                             sort = FALSE,
                                             threshold = NULL,
                                             standardize = TRUE,
-                                            reverse_items = NULL,
                                             verbose = TRUE,
                                             ...) {
   # save name of data set
@@ -307,20 +300,6 @@ principal_components.data.frame <- function(x,
   # remove missing
   x <- stats::na.omit(x)
 
-  # should some items be reversed?
-  if (!is.null(reverse_items)) {
-    # numeric indices should be replaced by their column names
-    if (is.numeric(reverse_items)) {
-      reverse_items <- colnames(x)[reverse_items]
-    }
-    if (verbose) {
-      insight::format_alert(paste("Reversing items:", toString(reverse_items)))
-    }
-    # reverse the items
-    x <- datawizard::reverse_scale(x, reverse_items, verbose = verbose)
-    original_data <- datawizard::reverse_scale(original_data, reverse_items, verbose = FALSE)
-  }
-
   # Select numeric only
   x <- x[vapply(x, is.numeric, TRUE)]
 
@@ -345,7 +324,6 @@ principal_components.data.frame <- function(x,
 
     attr(pca_loadings, "data") <- data_name
     attr(pca_loadings, "dataset") <- original_data
-    attr(pca_loadings, "reverse_items") <- reverse_items
 
     return(pca_loadings)
   }
@@ -469,7 +447,6 @@ principal_components.data.frame <- function(x,
   )
   attr(pca_loadings, "data") <- data_name
   attr(pca_loadings, "dataset") <- original_data
-  attr(pca_loadings, "reverse_items") <- reverse_items
 
   # add class-attribute for printing
   class(pca_loadings) <- unique(c("parameters_pca", "see_parameters_pca", class(pca_loadings)))

R/utils_pca_efa.R

@@ -13,10 +13,6 @@
 #' observation, else, the sum score of all (sub) items is calculated. If `NULL`,
 #' the value is chosen to match half of the number of columns in a data frame,
 #' i.e. no more than 50% missing values are allowed.
-#' @param reverse_items Character vector of variable names or numeric indices
-#' indicating their column positions in `x` that should be reversed before
-#' computing sum scores. This is useful when the items are not coded in the same
-#' direction. If `NULL` (default), no items are reversed.
 #' @param verbose Logical, whether to print messages about reversing items.
 #'
 #' @details
@@ -52,30 +48,10 @@
 #' (mtcars$hp + mtcars$qsec) / 2
 #'
 #' @export
-get_scores <- function(x, n_items = NULL, reverse_items = NULL, verbose = TRUE) {
+get_scores <- function(x, n_items = NULL) {
   subscales <- closest_component(x)
   dataset <- attributes(x)$dataset
 
-  # should some items be reversed?
-  if (!is.null(reverse_items)) {
-    # check if the object has an attribute "reverse_items" - if so, warn that
-    # we don't want to "double reverse" items
-    if ("reverse_items" %in% names(attributes(x))) {
-      insight::format_warning(
-        "It seem that items have already been reversed in this data set. Make sure that you do not reverse them again."
-      )
-    }
-    # numeric indices should be replaced by their column names
-    if (is.numeric(reverse_items)) {
-      reverse_items <- colnames(dataset)[reverse_items]
-    }
-    if (verbose) {
-      insight::format_alert(paste("Reversing items:", toString(reverse_items)))
-    }
-    # reverse the items
-    dataset <- datawizard::reverse_scale(dataset, reverse_items, verbose = verbose)
-  }
-
   out <- lapply(sort(unique(subscales)), function(.subscale) {
     columns <- names(subscales)[subscales == .subscale]
     items <- dataset[columns]

man/get_scores.Rd

@@ -7,34 +7,4 @@ test_that("get_scores", {
   scores <- get_scores(pca)
   expect_equal(head(scores$Component_1), c(38.704, 38.755, 28.194, 58.339, 78.658, 51.064), tolerance = 1e-2)
   expect_equal(head(scores$Component_2), c(63.23, 63.51, 55.805, 64.72, 96.01, 62.61), tolerance = 1e-2)
-
-  scores2 <- get_scores(pca, reverse_items = c("cyl", "drat"))
-  expect_equal(head(scores2$Component_1), c(38.682, 38.733, 28.992, 58.645, 78.136, 51.498), tolerance = 1e-2)
-
-  expect_warning(
-    get_scores(pca, reverse_items = c("cyl", "abc")),
-    regex = "Following variable(s)",
-    fixed = TRUE
-  )
-
-  expect_message(
-    get_scores(pca, reverse_items = c("cyl", "drat")),
-    regex = "Reversing items: cyl, drat",
-    fixed = TRUE
-  )
-
-  expect_silent(get_scores(pca, reverse_items = c("cyl", "abc"), verbose = FALSE))
-  expect_silent(get_scores(pca, reverse_items = c("cyl", "drat"), verbose = FALSE))
-
-  pca <- principal_components(
-    mtcars[, 1:7],
-    n = 2,
-    rotation = "varimax",
-    reverse_items = c("cyl", "drat")
-  )
-  expect_warning(
-    get_scores(pca, reverse_items = c("cyl", "drat")),
-    regex = "It seems that",
-    fixed = TRUE
-  )
 })