pca_nipals: Principal components based on the non-linear iterative...

In l-ramirez-lopez/resemble: Memory-Based Learning in Spectral Chemometrics

Principal components based on the non-linear iterative partial least squares (nipals) algorithm

Description

Computes orthogonal socres partial least squares (opls) regressions with the NIPALS algorithm. It allows multiple response variables. For internal use only!

Usage

pca_nipals(X, ncomp, center, scale,
           maxiter, tol,
           pcSelmethod = "var",
           pcSelvalue = 0.01)

Arguments

X	a matrix of predictor variables.
ncomp	the number of pls components.
scale	logical indicating whether X must be scaled.
maxiter	maximum number of iterations.
tol	limit for convergence of the algorithm in the nipals algorithm.
pcSelmethod	the method for selecting the number of components. Options are: 'cumvar' (for selecting the number of principal components based on a given cumulative amount of explained variance) and "var" (for selecting the number of principal components based on a given amount of explained variance). Default is 'var'
pcSelvalue	a numerical value that complements the selected method (pcSelmethod). If "cumvar" is chosen, it must be a value (larger than 0 and below 1) indicating the maximum amount of cumulative variance that the retained components should explain. If "var" is chosen, it must be a value (larger than 0 and below 1) indicating that components that explain (individually) a variance lower than this threshold must be excluded. If "manual" is chosen, it must be a value specifying the desired number of principal components to retain. Default is 0.01.
Y	a matrix of either a single or multiple response variables.

Value

a list containing the following elements:

• pc_scores a matrix of principal component scores.

• pc_loadings a matrix of of principal component loadings.

• variance a matrix of the variance of the principal components.

• scale a list conating two objects: center and scale, which correspond to the vectors used to center and scale the input matrix.

Author(s)

Leonardo Ramirez-Lopez

l-ramirez-lopez/resemble documentation built on April 20, 2023, 10:44 p.m.