simpls
Purpose
Partial Least Squares regression using the SIMPLS algorithm.
Synopsis
[reg,ssq,xlds,ylds,wts,xscrs,yscrs,basis] = simpls(x,y,ncomp,options)
options = simpls('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');.
See Also
nippls, pcr, pls, plsnipal
