nzilbb.vowels (version 0.3.1)

correlation_test: Permutation test of pairwise correlations

Description

Permute data a given number (n) of times, collecting pairwise correlations and testing them for significance. See plot_correlation_magnitudes() and plot_correlation_counts() for plotting functions which take the output of this function.

Usage

correlation_test(pca_data, n = 100, cor.method = "pearson")

Value

object of class correlation_test, with attributes:

• $permuted_correlations A tibble of length n of pairs from the original data, their correlations, and the significance of each correlation (as p-values).

• $actual_correlations the correlations of each pair of variables in the original data and their significance (as p-values).

• $iterations the number of permutations carried out.

• $cor_method the form of correlation used.

Arguments

pca_data

dataframe or matrix containing only continuous variables. (as accepted by the prcomp function.)

n

the number of times (integer) to permute that data. Warning: high values will take a long time to compute. Default: 100.

cor.method

method to use for correlations (default = "pearson"). Alternative is "spearman" (see ?cor.test).

Examples

  # get a small sample of random intercepts.
  pca_data <- onze_intercepts |>
    dplyr::select(-speaker) |>
    dplyr::slice_sample(n=10)

  # apply correlation test with 10 permutations.
  # actual use requires at least 100.
  cor_test <- correlation_test(pca_data, n = 10, cor.method = 'pearson')
  # Return summary of significant correlations
  summary(cor_test)

  # use spearman correlation instead.
  cor_test_spear <- correlation_test(pca_data, n = 10, cor.method = 'spearman')