Plot Scores from Significant PCs Against PCA Input

It is sometimes useful to see the relationship between PCs and the raw values of the input data fed into PCA. This function takes the results of running pca_test, the scores for each speaker from the pca object, and the raw data fed into the PCA analysis. In the usual model-to-pca analysis pipeline, the resulting plot depicts by-speaker random intercepts for each vowel and an indication of which variables are significantly loaded onto the PCs. It allows the researcher to visualise the strength of the relationship between intercepts and PC scores.

Usage

plot_pc_input(pca_object, pca_data, pca_test)

Arguments

pca_object

Output of prcomp.

pca_data

Data fed into prcomp. This should not include speaker identifiers.

pca_test

Output of pca_test

Value

a ggplot object.

Examples

pca_data <- onze_intercepts |> dplyr::select(-speaker)
onze_pca <- prcomp(pca_data, scale = TRUE)
onze_pca_test <- pca_test(pca_data, n = 10)
plot_pc_input(onze_pca, pca_data, onze_pca_test)
#> `geom_smooth()` using formula = 'y ~ x'