Get plot with factor analysis result — factor

PCA is performed using add_factors.

Usage

factor_plot(
  data,
  cols,
  newcols = NULL,
  k = 2,
  method = "pca",
  reorder = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

data: A dataframe.
cols: A tidy selection of item columns. If the first column already contains a pca from add_factors, the result is used. Other parameters are ignored. If there is no pca result yet, it is calculated by add_factors first.
newcols: Names of the factor columns as a character vector. Must be the same length as k or NULL. Set to NULL (default) to automatically build a name from the common column prefix, prefixed with "fct_", postfixed with the factor number.
k: Number of factors to calculate. Set to NULL to generate a scree plot with eigenvalues for all components up to the number of items and automatically choose k. Eigenvalues and the decision on k are calculated by psych::fa.parallel.
method: The method as character value. Currently, only pca is supported.
reorder: Reorder items to minimize line crossings, Either TRUE to automatically select a method ("olo" if seriation is installed, otherwise "min"), or one of the character values "max", "min", "spread", "gw", or "olo". Defaults to FALSE which disables reordering.
labels: If TRUE (default) extracts labels from the attributes, see codebook.
clean: Prepare data by data_clean.
...: Placeholder to allow calling the method with unused parameters from plot_metrics.

A ggplot object.

library(volker)
ds <- volker::chatgpt

volker::factor_plot(ds, starts_with("cg_adoption"), k = 3)

#> In the plot, 4 missing case(s) omitted.