basis.ACP: A function to decompose multivariate data by principal...

In multisensi: Multivariate Sensitivity Analysis

A function to decompose multivariate data by principal components analysis (PCA)

Description

The basis.ACP function decomposes a multivariate data set according to principal components analysis.

Usage

basis.ACP(simuls, basis.args = list())

Arguments

simuls	a data.frame of size N x T, typically a set of N simulation outputs of length T.
basis.args	an empty list of arguments for the PCA decomposition.

Details

This function uses prcomp.

Value

H	a data.frame of size N x T, containing the coefficients of the PCA decomposition. It is equal to the x output of function prcomp.
L	a matrix of size T x T. It contains the eigenvectors of the PCA decomposition.
call.info	list with the element reduction="pca"

See Also

prcomp

Examples

data(biomasseY)
res <- basis.ACP(biomasseY)

multisensi documentation built on Aug. 20, 2026, 1:08 a.m.