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Run permutation test on PCA analysis.

Source: R/permutation_test.R
permutation_test.Rd

[Superseded] Permute data fed to PCA a given number of times, collecting the number of significant pairwise correlations in the permuted data and the variances explained for a given number of PCs.

Usage

permutation_test(
  pca_data,
  pc_n = 5,
  n = 100,
  scale = TRUE,
  cor.method = "pearson"
)

Arguments

pca_data

data fed to the prcomp function. Remove non-continuous variables.

pc_n

the number of PCs to collect variance explained from.

n

the number of times to permute that data. Warning: high values will take a long time to compute.

scale

whether the PCA variables should be scaled (default = TRUE).

cor.method

method to use for correlations (default = "pearson"). Alternative is "spearman".

Value

object of class permutation_test

  • $permuted_variances n x pc_no matrix of variances explained by first pc_no PCs in n permutations of original data.

  • $permuted_correlations list of length n of significant pairwise correlations in n permutations of the data (<= 0.05).

  • $actual_variances pc_n x 2 tibble of variances explained by first pc_n PCs with original data.

  • $actual_correlations the number of significant pairwise correlations (<= 0.05) in the original data.

Details

This function is now superseded. Use correlation_test() for pairwise correlations and pca_test() for variance explained and loadings.

Examples

permutation_test(
  onze_intercepts |> dplyr::select(-speaker),
  pc_n = 5,
  n = 10,
  scale = TRUE,
  cor.method = 'pearson'
 )
#> $permuted_variances
#>           PC1        PC2        PC3        PC4        PC5
#> 1  0.09801421 0.08056611 0.07383211 0.07122915 0.06337760
#> 2  0.09673745 0.08748429 0.08218244 0.07273270 0.06843527
#> 3  0.08415248 0.08005626 0.07689180 0.07425076 0.06783985
#> 4  0.09621459 0.08328628 0.07413359 0.06964632 0.06780199
#> 5  0.09871256 0.09141129 0.08090679 0.06925790 0.06825858
#> 6  0.08788232 0.08679001 0.07776683 0.07676907 0.06630793
#> 7  0.08640135 0.08213955 0.08045114 0.07182490 0.06707714
#> 8  0.09262402 0.08927014 0.08010901 0.07848387 0.06908269
#> 9  0.10197223 0.08434247 0.07607760 0.07194075 0.06899879
#> 10 0.09522405 0.08906267 0.07596028 0.07020678 0.06643209
#> 
#> $permuted_correlations
#>  [1]  8  7  5  8 10  6  8  9  9 12
#> 
#> $actual_variances
#> # A tibble: 5 × 2
#>   PC    variance_explained
#>   <chr>              <dbl>
#> 1 PC1               0.183 
#> 2 PC2               0.143 
#> 3 PC3               0.0989
#> 4 PC4               0.0835
#> 5 PC5               0.0774
#> 
#> $actual_correlations
#> [1] 69
#> 
#> attr(,"class")
#> [1] "permutation_results"