Generates a matrix of PLS models for MSPC.
- coeff = plsrsgn(data,lv,out)
For a given matrix of data data, this function calculates a partial least squares (PLS) regression model of each variable in the data, using all of the remaining variables in the data. 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 plsrsgn.
- data = matrix of input data
- lv = maximum number of PLS latent variables to calculate
- out = allows the user to suppress intermediate output [out=0 suppresses output]
- coeff = matrix of PLS regression vectors