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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[Superseded] Permute data fed to PCA a given number of times, collecting the number of significant pairwise correlations in the permuted data and the variances explained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This function is now superseded.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  0.10087601 0.08879798 0.08032575 0.07587374 0.07218578
#> 2  0.10033966 0.09083708 0.08173040 0.07139712 0.06669756
#> 3  0.08842098 0.07999117 0.07854846 0.07381645 0.07092127
#> 4  0.10318165 0.08848091 0.07999481 0.07450134 0.06900145
#> 5  0.09295884 0.08456273 0.08102493 0.07494879 0.07029683
#> 6  0.09180853 0.08459227 0.07765252 0.07313609 0.07019674
#> 7  0.09942178 0.08360813 0.07871929 0.07291580 0.06770617
#> 8  0.09378021 0.08558982 0.07711176 0.06803352 0.06344125
#> 9  0.09087670 0.08223823 0.07581839 0.07071398 0.06941024
#> 10 0.09174463 0.08944009 0.08107437 0.07628478 0.06726242
#> 
#> $permuted_correlations
#>  [1] 11 12  9 13  8  6 11  7  5  9
#> 
#> $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"