multinomCV4CA: Compute the permuted and bootstrapped eigenvalues of the...

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

Compute the permuted and bootstrapped eigenvalues of the correspondence analysis (CA) of a matrix suitable for CA (i.e., a matrix with non negative elements).

Description

multinomCV4CA: a very fast routine that computes the permuted and bootstrapped eigenvalues of the correspondence analysis (CA) of a matrix suitable for CA (i.e., a matrix with non negative elements).

Usage

multinomCV4CA(Data, niter = 1000)

Arguments

Data	a matrix suitable for CA (i.e., a matrix with non negative elements).
niter	number of Bootstrapped/Permutations (Default: 1000).

Details

multinomCV4CA uses the multinomial distribution to simulate bootstrap and permutation resampling for a correspondence analysis.

Value

A list with two elements: 1) $Permuted.ValP: The matrix of the niter by rank(Data) permuted eigenvalues of the data matrix Data, and $Bootstraped.ValP: The matrix of the niter by rank(Data) bootstrapped eigenvalues of the data matrix Data.

Examples

## Not run: 
set.seed(87) # set the seed
X <- matrix(round(runif(21)*20), ncol = 3) # good for CA
ResCV <- multinomCV4CA(X)

## End(Not run)

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