fastPerm4CA: Permutation test for CA on real contingency tables obtained...

In HerveAbdi/data4PCCAR: Some data sets and R-functions for PCA and CA to accompany Abdi & Beaton (to appear, 2023) Principal Component and Correspondence Analysis with R

Permutation test for CA on real contingency tables obtained from a multinomial distribution.

Description

fastPerm4CA computes a permutation test for CA when CA is performed on a real contingency table. The resampling is obtained from a multinomial distribution. fastPerm4CA can be used for big tables for the omnibus test (i.e., inertia) and for the test on the eigenvalues.

Usage

fastPerm4CA(X, nIter = 1000, compact = FALSE)

Arguments

X	the data matrix (non-negative integers)
nIter	(Default = 1000). Number of Iterations (i.e. number of permuted samples computed).
compact	if TRUE return only p-values for omnibus test default is FALSE.

Value

a list with fixedInertia: the CA-inertia of the data matrix; fixedEigenvalues: the CA-eigenvalues of the data matrix; pOmnibus: the probability associated to the inertia. If compact is FALSE, return also permInertia: an nIter * 1 vector containing the permuted inertia; pEigenvalues: The probabilities associated to each eigenvalue; If compact is FALSE, return also permEigenvalues: an nIter * L matrix giving the permuted eigenvalues.

Author(s)

Hervé Abdi

HerveAbdi/data4PCCAR documentation built on Sept. 11, 2022, 4:19 p.m.