Plsrsgn: Difference between revisions

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===Purpose===
===Purpose===


Generates a matrix used to calculate residuals from a single data block using partial least squares regression models.
Generates a matrix of PLS models for MSPC.


===Synopsis===
===Synopsis===
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===Description===
===Description===


coeff = plsrsgn(data,lv) calculates a matrix coeff from a single data block data. plsrsgn calculates partial least squares regression models of each variable in the matrix data using the remaining variables and the number of latent variables lv. 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.
For a given matrix of data <tt>data</tt>, 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 <tt>coeff</tt>, which can be used like the <tt>I=PP'</tt> matrix in PCA.
 
Multiplying a new data matrix by the matrix <tt>coeff</tt> yields a matrix whose values are the difference between the new data and it's prediction based on the PLS regressions created by <tt>plsrsgn</tt>.
 
====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


===See Also===
===See Also===


[[plsrsgcv]], [[replace]]
[[plsrsgcv]], [[replace]]

Latest revision as of 14:45, 10 October 2008

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

See Also

plsrsgcv, replace