Simpls

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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, plsnipal, preprocess, analysis

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