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.
Usage
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.
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)
