opls_get_all: orthogonal scores algorithn of partial leat squares...

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

orthogonal scores algorithn of partial leat squares (opls_get_all)

Description

Computes orthogonal socres partial least squares (opls_get_all) regressions with the NIPALS algorithm. It retrives a comprehensive set of pls outputs (e.g. vip and sensivity radius). It allows multiple response variables. NOTE: For internal use only!

Usage

opls_get_all(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:

• ncomp the number of components used.

• coefficients the matrix of regression coefficients.

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

• scores the matrix of scores.

• X_loadings the matrix of X loadings.

• Y_loadings the matrix of Y loadings.

• vip the projection matrix.

• selectivity_ratio the matrix of selectivity ratio (see Rajalahti, Tarja, et al. 2009).

• Y the Y input.

• variance a list conating two objects: x_var and y_var. These objects contain information on the explained variance for the X and Y matrices respectively.

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

• weights the matrix of wheights.

Author(s)

Leonardo Ramirez-Lopez

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