opls_get_all: orthogonal scores algorithn of partial leat squares...
In resemble: Memory-Based Learning in Spectral Chemometrics

opls_get_all

R Documentation

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.