Simpls
Jump to navigation
Jump to search
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".
OPIONAL INPUTS:
- ncomp = integer, number of latent variables to use in {default = rank of X-block}, and
options = a structure array discussed below.
OUPUTS:
- 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.
The default options can be retreived using: options = simpls('options');.