plot_loadings {nzilbb.vowels} | R Documentation |
Plot PC index loadings from pca_test
object.
Description
Index loadings (Vieira 2012) are presented with confidence intervals on the sampling distribution generated by bootstrapping and a null distribution generated by permutation.
Usage
plot_loadings(
pca_test,
pc_no = 1,
violin = FALSE,
filter_boots = FALSE,
quantile_threshold = 0.25
)
Arguments
pca_test |
an object of class pca_test_results generated by |
pc_no |
An integer indicating which PC to plot. |
violin |
If TRUE, violin plots are added for the confidence intervals of the sampling distribution. |
filter_boots |
if TRUE, only bootstrap iterations in which the variable
with the highest median loading is above |
quantile_threshold |
a real value between 0 and 1. Use this to change the threshold used for filtering bootstrap iterations. The default is 0.25. |
Details
If PCs are unstable, there is an option (filter_boots
) to take only the
bootstrap iterations in which the variable with the highest median loading
across all iterations is above quantile_threshold
(default: 0.25). This
helps to reveal reliable connections of this variable with other variables in
the data set.
Value
ggplot
object.
References
Vieira, Vasco (2012): Permutation tests to estimate significances on Principal Components Analysis. Computational Ecology and Software 2. 103–123.
Examples
onze_pca <- pca_test(onze_intercepts |> dplyr::select(-speaker), n = 10)
# Plot PC1
plot_loadings(onze_pca, pc_no=1)
# Plot PC2 with violins (not particularly useful in this case!)
plot_loadings(onze_pca, pc_no=2, violin = TRUE)