permutation_test {nzilbb.vowels} | R Documentation |
Run permutation test on PCA analysis.
Description
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 |
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". |
Details
This function is now superseded. Use correlation_test()
for pairwise
correlations and pca_test()
for variance explained and loadings.
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.
Examples
permutation_test(
onze_intercepts |> dplyr::select(-speaker),
pc_n = 5,
n = 10,
scale = TRUE,
cor.method = 'pearson'
)