permutationPA: Permutation Parallel Analysis

In jackstraw: Statistical Inference for Unsupervised Learning

Permutation Parallel Analysis

Description

Estimate a number of significant principal components from a permutation test.

Usage

permutationPA(dat, B = 100, threshold = 0.05, verbose = TRUE)

Arguments

dat	a data matrix with m rows as variables and n columns as observations.
B	a number (a positive integer) of resampling iterations.
threshold	a numeric value between 0 and 1 to threshold p-values.
verbose	a logical indicator as to whether to print the progress.

Details

Adopted from sva::num.sv, and based on Buja and Eyuboglu (1992)

Value

permutationPA returns

r	an estimated number of significant principal components based on thresholding p-values at threshold
p	a list of p-values for significance of principal components

References

Buja A and Eyuboglu N. (1992) Remarks on parallel analysis. Multivariate Behavioral Research, 27(4), 509-540

jackstraw documentation built on Sept. 17, 2024, 1:07 a.m.