opls_get_basics: fast orthogonal scores algorithn of partial leat squares...

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

fast orthogonal scores algorithn of partial leat squares (opls)

Description

Computes orthogonal socres partial least squares (opls) regressions with the NIPALS algorithm. It allows multiple response variables. In contrast to codeopls function, this one does not compute unnecessary data for (local) regression. For internal use only!

Usage

opls_get_basics(X, Y, ncomp, scale, 
                maxiter, tol, 
                algorithm = "pls", 
                xls_min_w = 3, 
                xls_max_w = 15)

Arguments

X	a matrix of predictor variables.
Y	a matrix of either a single or multiple response 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.
algorithm	(for weights computation) a character string indicating what method to use. Options are: 'pls' for pls (using covariance between X and Y), 'mpls' for modified pls (using correlation between X and Y) or 'xls' for extended pls (as implemented in BUCHI NIRWise PLUS software).
xls_min_w	(for weights computation) an integer indicating the minimum window size for the "xls" method. Only used if algorithm = 'xls'. Default is 3 (as in BUCHI NIRWise PLUS software).
xls_max_w	(for weights computation) an integer indicating the maximum window size for the "xls" method. Only used if algorithm = 'xls'. Default is 15 (as in BUCHI NIRWise PLUS software).

Value

a list containing the following elements:

• coefficients the matrix of regression coefficients.

• bo a matrix of one row containing the intercepts for each component.

• Y_loadings the matrix of Y loadings.

• projection_mat the projection matrix.

• transf a list conating two objects: Xcenter and Xscale.

Author(s)

Leonardo Ramirez-Lopez

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