opls: orthogonal scores algorithn of partial leat squares (opls)
In resemble: Memory-Based Learning in Spectral Chemometrics

View source: R/RcppExports.R

opls	R Documentation

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. It does not return the variance information of the components. NOTE: For internal use only!

Usage

opls(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).