Permutation test of pairwise correlations

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")

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).

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.

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)
#> Correlation test results.
#> Count of significant pairwise correlations in original data at alpha = 0.05: 14
#> Mean significant pairwise correlations in permuted data (n = 10) at alpha = 0.05: 9.5
#> Min = 7, Max = 13.
#> 
#> Top 5 pairwise correlations in original data:
#> F2_LOT, F2_THOUGHT: -0.9
#> F1_GOOSE, F2_START: 0.81
#> F1_GOOSE, F2_THOUGHT: -0.78
#> F2_DRESS, F2_KIT: 0.76
#> F2_FLEECE, F2_NURSE: -0.75

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