Generates a PLS model for MSPC with cross-validation.
- coeff = plsrsgcv(data,lv,cvit,cvnum,out)
This function constructs a matrix of PLS models that can be used like a PCA model for multivariate statistical process control (MSPC) purposes. Given a data matrix data, a PLS model is formed using a maximum of lv latent variables that relates each variable to all of the other variables. The PLS model regression vectors are collected in an output matrix coeff, which can be used like the I=PP' matrix in PCA.
Multiplying a new data matrix by the matrix coeff yields a matrix whose values are the difference between the new data and it's prediction based on the PLS regressions created by plsrsgncv.
Warning: This function can take a long time to execute if you choose to do many cross-validations! Execution can be sped up by setting optional variable out=0.
- data = input data matrix
- lv = maximum number of latent variables to consider
- cvit = the number of cross-validation test sets
- cvnum = the number of samples in each cross validation test set
- out = allows the user to suppress intermediate output [out=0 suppresses output]
- coeff = matrix of PLS regression coefficients