Plot of correlation counts from correlation_test object

Plot the number of statistically significant pairwise correlations in a data set given an alpha value against the distribution of counts of statistically significant pairwise correlations in permuted data. This is an informal test which is useful to convincing yourself that there is structure in your data which PCA might be able to uncover.

Usage

plot_correlation_counts(cor_test, alpha = 0.05, half_violin = FALSE)

Arguments

cor_test

an object of class correlation_test generated by correlation_test.

alpha

significance level for counting correlation as significant.

half_violin

Plot correlation counts using a half violin plot and half point plot. Quantiles are not currently supported.

Value

ggplot object.

Details

The resulting plot presents the distribution of counts of statistically significant correlations at a given alpha level in the permuted data and the count of statistically significant correlations in the original data. If the red dot is above the uppermost line inside the blue violin plot, we say the number of statistically significant correlations in the real data is itself statistically significant. Usually this is used as a rough sanity check in the course of a PCA workflow and we want to see the red dot well above the violin (as in the example below).

The resulting plot is a ggplot2 plot and can be modified using functions from that package. For instance, titles can be removed using the ggplot2::labs() function (as in the examples below).

Examples

  # Test correlations (use at least n = 100)
  cor_test <- correlation_test(onze_intercepts |>
    dplyr::select(-speaker), n = 10)
  cor_plot <- plot_correlation_counts(cor_test)
  cor_plot


  # make statistical test more strict by reducing the alpha.
  cor_plot_strict <- plot_correlation_counts(cor_test, alpha = 0.01)

  # modify plot using `ggplot2` functions, e.g.
  cor_plot_strict +
    ggplot2::labs(title = NULL) +
    ggplot2::theme_bw()