plot_variance_explained function

Create plot of variances explained from pca_test object

The variance explained by each PC in a dataset is plotted with confidence intervals generated by bootstrapping and a null distribution generated by permutation. The function accepts the result of calling the pca_test

function.

plot_variance_explained(pca_test, pc_max = NA, percent = TRUE)

Arguments

• pca_test: an object of class pca_test_results generated by pca_test.
• pc_max: the maximum number of PCs to plot. If NA, plot all PCs.
• percent: if TRUE, represent variance explained as a percentage. If FALSE, represent as eigenvalues.

Returns

ggplot object.

Details

By default, variance explained is represented as a percentage. If the argument percent is set to FALSE, then the variance explained is represented by the eigenvalues corresponding to each PC.

Examples

onze_pca <- pca_test(onze_intercepts |> dplyr::select(-speaker), n = 10)
  # Plot with percentages
  plot_variance_explained(onze_pca)
  # Plot with eigenvalues and only the first 5 PCs.
  plot_variance_explained(onze_pca, pc_max = 5, percent = FALSE)