Simpls

Purpose

Partial Least Squares regression using the SIMPLS algorithm.

Synopsis

[reg,ssq,xlds,ylds,wts,xscrs,yscrs,basis] = simpls(x,y,ncomp,options)

Description

SIMPLS performs PLS regression using SIMPLS algorithm.

Inputs

• x = X-block (predictor block) class "double" or "dataset", and
• y = Y-block (predicted block) class "double" or "dataset".

Optional Inputs

• ncomp = integer, number of latent variables to use in {default = rank of X-block}, and
• options = a structure array discussed below.

Outputs

• reg = matrix of regression vectors,
• ssq = the sum of squares captured (ssq),
• xlds = X-block loadings,
• ylds = Y-block loadings,
• wts = X-block weights,
• xscrs = X-block scores,
• yscrs = Y-block scores, and
• basis = the basis of X-block loadings.

NOTE: The regression matrices are ordered in reg such that each Ny (number of Y-block variables) rows correspond to the regression matrix for that particular number of latent variables.

NOTE: in previous versions of SIMPLS, the X-block scores were unit length and the X-block loadings contained the variance. As of Version 3.0, this algorithm now uses standard convention in which the X-block scores contain the variance.

Options

options = a structure array with the following fields:

• display: [ {'on'} | 'off' ], governs level of display, and
• ranktest: [ 'none' | 'data' | 'scores' | {'auto'} ], governs type of rank test to perform.

'data' = single test on X-block (faster with smaller data blocks and more components),

'scores' = test during regression on scores matrix (faster with larger data matricies),

'auto' = automatic selection, or

'none' = assumes X-block has sufficient rank.

See Also

crossval, modelstruct, pcr, pls, plsnipal, preprocess, analysis