Permutation test for PLSC latent variables — perm

Uses row-wise permutation of the Y block to assess the significance of each latent variable (LV) in a fitted plsc model. The test statistic is the singular value of the cross-covariance matrix for each LV.

Usage

# S3 method for class 'plsc'
perm_test(
  x,
  X,
  Y,
  nperm = 1000,
  comps = ncomp(x),
  stepwise = TRUE,
  shuffle_fun = NULL,
  parallel = FALSE,
  alternative = c("greater", "less", "two.sided"),
  alpha = 0.05,
  ...
)

Arguments

x: A fitted plsc model object.
X: Original X block used to fit x.
Y: Original Y block used to fit x.
nperm: Number of permutations to perform (default 1000).
comps: Number of components (LVs) to test. Defaults to ncomp(x).
stepwise: Logical; if TRUE (default), perform sequential testing with deflation.
shuffle_fun: Optional function to permute Y; defaults to shuffling rows.
parallel: Logical; if TRUE, use parallel processing via future.apply.
alternative: Character string for the alternative hypothesis: "greater" (default), "less", or "two.sided".
alpha: Significance level used to report n_significant; not used directly in p-value calculation.
...: Additional arguments (currently unused).