default oblimin

Author: strengejacke

NEWS.md

@@ -4,6 +4,11 @@
 
 * The `standardize` argument in `factor_analysis()` now defaults to `FALSE`.
 
+* The `rotation` argument in `factor_analysis()` now defaults to `"oblimin"`,
+  because the former default of `"none"` rarely makes sense in the context of
+  factor analysis. If you want to use no rotation, please set `rotation =
+  "none"`.
+
 * The `cor` argument in `n_factors()` was renamed into `correlation_matrix`. In
   `factor_analysis()`, the `cor` argument was completely removed to avoid naming
   collision with the `cor` argument of `psych::fa()`, which now users can pass

R/factor_analysis.R

@@ -9,7 +9,7 @@ factor_analysis <- function(x, ...) {
 #' @export
 factor_analysis.data.frame <- function(x,
                                        n = "auto",
-                                       rotation = "none",
+                                       rotation = "oblimin",
                                        sort = FALSE,
                                        threshold = NULL,
                                        standardize = FALSE,

R/principal_components.R

@@ -19,7 +19,8 @@
 #' @param rotation If not `"none"`, the PCA / FA will be computed using the
 #'   **psych** package. Possible options include `"varimax"`, `"quartimax"`,
 #'   `"promax"`, `"oblimin"`, `"simplimax"`, or `"cluster"` (and more). See
-#'   [`psych::fa()`] for details.
+#'   [`psych::fa()`] for details. The default is `"none"` for PCA, and
+#'   `"oblimin"` for FA.
 #' @param sparse Whether to compute sparse PCA (SPCA, using [`sparsepca::spca()`]).
 #'   SPCA attempts to find sparse loadings (with few nonzero values), which improves
 #'   interpretability and avoids overfitting. Can be `TRUE` or `"robust"` (see

man/principal_components.Rd

@@ -103,7 +103,7 @@ test_that("predict model_parameters fa", {
     predict(mp, names = c("A", "B", "C", "D", "E"), keep_na = FALSE),
     c("A", "B", "C", "D", "E")
   )
-  model <- factor_analysis(d, n = 5)
+  model <- factor_analysis(d, n = 5, rotation = "none")
   expect_identical(nrow(predict(model, keep_na = FALSE)), 2436L)
 })