PCA with n = 1

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.2
+Version: 0.21.2.1
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -1,3 +1,10 @@
+# parameters 0.21.3
+
+## Changes
+
+* `principal_components()` and `factor_analysis()` now also work when argument
+  `n = 1`.
+
 # parameters 0.21.2
 
 ## Changes

R/principal_components.R

@@ -7,13 +7,14 @@
 #' Details section).
 #'
 #' @param x A data frame or a statistical model.
-#' @param n Number of components to extract. If `n="all"`, then `n` is
-#'   set as the number of variables minus 1 (`ncol(x)-1`). If
-#'   `n="auto"` (default) or `n=NULL`, the number of components is
-#'   selected through [`n_factors()`] resp. [`n_components()`].
-#'   In [`reduce_parameters()`], can also be `"max"`, in which case
-#'   it will select all the components that are maximally pseudo-loaded (i.e.,
-#'   correlated) by at least one variable.
+#' @param n Number of components to extract. If `n="all"`, then `n` is set as
+#'   the number of variables minus 1 (`ncol(x)-1`). If `n="auto"` (default) or
+#'   `n=NULL`, the number of components is selected through [`n_factors()`] resp.
+#'   [`n_components()`]. Else, if `n` is a number, `n` components are extracted.
+#'   If `n` exceeds number of variables in the data, it is automatically set to
+#'   the maximum number (i.e. `ncol(x)`). In [`reduce_parameters()`], can also
+#'   be `"max"`, in which case it will select all the components that are
+#'   maximally pseudo-loaded (i.e., correlated) by at least one variable.
 #' @param rotation If not `"none"`, the PCA / FA will be computed using the
 #'   **psych** package. Possible options include `"varimax"`,
 #'   `"quartimax"`, `"promax"`, `"oblimin"`, `"simplimax"`,
@@ -129,7 +130,7 @@
 #' magnitude. In other words, the eigenvalues explain the variance of the
 #' data along the new feature axes.
 #'
 #' @examplesIf require("nFactors", quietly = TRUE) && require("sparsepca", quietly = TRUE) && require("psych", quietly = TRUE)
 #' library(parameters)
 #'
 #' \donttest{
@@ -263,7 +264,7 @@
   # Catch and compute Rotated PCA
   if (rotation != "none") {
     if (sparse) {
       insight::format_error("Sparse PCA is currently incompatible with rotation. Use either `sparse=TRUE` or `rotation`.")
     }
 
     loadings <- .pca_rotate(
@@ -418,15 +419,9 @@
   } else if (n == "all") {
     n <- ncol(x) - 1
   } else if (n >= ncol(x)) {
-    n <- ncol(x) - 1
-  }
-
-  ## TODO: the next if-statement was removed by Dom, but this breaks
-  ## performance code. Need to check, so we for now add this back
-
-  # sanity check - we need at least two factors
-  if (n < 2 && ncol(x) >= 2) {
-    n <- 2
+    n <- ncol(x)
+  } else if (n < 1) {
+    n <- 1
   }
   n
 }
@@ -442,7 +437,7 @@
                         original_data = NULL,
                         ...) {
   if (!(rotation %in% c("varimax", "quartimax", "promax", "oblimin", "simplimax", "cluster", "none"))) {
     insight::format_error("`rotation` must be one of \"varimax\", \"quartimax\", \"promax\", \"oblimin\", \"simplimax\", \"cluster\" or \"none\".")
   }
 
   if (!inherits(x, c("prcomp", "data.frame"))) {

tests/testthat/test-pca.R

@@ -19,10 +19,17 @@ test_that("principal_components", {
     tolerance = 0.01
   )
 
-  expect_equal(
-    colnames(x),
-    c("Variable", "RC1", "RC2", "Complexity", "Uniqueness", "MSA")
-  )
+  expect_named(x, c("Variable", "RC1", "RC2", "Complexity", "Uniqueness", "MSA"))
+})
+
+
+test_that("principal_components, n", {
+  data(iris)
+  x <- parameters::principal_components(iris[1:4], n = 2)
+  expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
+
+  x <- parameters::principal_components(iris[1:4], n = 1)
+  expect_named(x, c("Variable", "PC1", "Complexity"))
 })
 
 
@@ -43,17 +50,13 @@ test_that("principal_components", {
     tolerance = 0.01
   )
 
-  expect_equal(
-    colnames(x),
-    c("Variable", "PC1", "PC2", "Complexity")
-  )
+  expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
 })
 
 
 # predict ----------------------
 # N.B tests will fail if `GPArotation` package is not installed
 
-require("GPArotation", quietly = TRUE)
 d <- na.omit(psych::bfi[, 1:25])
 model <- psych::fa(d, nfactors = 5)
 mp <- model_parameters(model, sort = TRUE, threshold = "max")