# Plsrsgn

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### Purpose

Generates a matrix of PLS models for MSPC.

### Synopsis

- coeff = plsrsgn(data,lv,out)

### Description

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`.

#### Inputs

**data**= matrix of input data**lv**= maximum number of PLS latent variables to calculate

#### Optional Inputs

**out**= allows the user to suppress intermediate output [out=0 suppresses output]

#### Outputs

**coeff**= matrix of PLS regression vectors