Dspls
From Eigenvector Documentation Wiki
Contents
Purpose
Partial Least Squares computational engine using Direct Scores algorithm.
Synopsis
- [reg,ssq,xlds,ylds,wts,xscrs,yscrs,basis] = dspls(x,y,ncomp,options)
Description
Performs PLS regression using Direct Scores PLS algorithm as described in Andersson, "A comparison of nine PLS1 algorithms", J. Chemometrics, (www.interscience.wiley.com) DOI: 10.1002/cem.1248
This modified SIMPLS algorithm provides improved numerical stability for high numbers of latent variables.
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.
Inputs
- x = X-block (predictor block) class "double".
- y = Y-block (predicted block) class "double".
Optional Inputs
- ncomp = the number of latent variables to be calculated (positive integer scalar {default = rank of X-block}.
Outputs
- reg = matrix of regression vectors.
- ssq = the sum of squares captured.
- xlds = X-block loadings.
- ylds = Y-block loadings.
- wts = X-block weights, currently returns empty.
- xscrs = X-block scores.
- yscrs = Y-block scores, currently returns empty.
- basis = the basis of X-block loadings.
Options
options = a structure array with the following fields:
- display : [ 'off' |{'on'}] governs display to command window
- 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' = auto selection, or 'none' = assume sufficient rank.