Polypls: Difference between revisions
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POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation. | POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation. | ||
Use POLYPRED to make predictions with new data. | Use POLYPRED to make predictions with new data. | ||
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* '''t''' = x-block scores, | * '''t''' = x-block scores, | ||
* '''u''' = y-block scores, | * '''u''' = y-block scores, | ||
* '''b''' = matrix of inner-relation | * '''b''' = matrix of polynomial coefficients for the inner-relation, | ||
* ''' | * '''ssqdif''' = table of x- and y-block variance captured by the PLS model. | ||
===Options=== | ===Options=== | ||
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options = a structure array with the following fields: | options = a structure array with the following fields: | ||
* ''' | * '''display''': [ 'off' | {'on'} ] governs display of SSQ table | ||
===See Also=== | ===See Also=== | ||
[[lwrxy]], [[pls]], [[polypred]] | [[lwrxy]], [[pls]], [[polypred]] | ||
Latest revision as of 14:04, 9 June 2014
Purpose
Calculate partial least squares regression models with polynomial inner relations.
Synopsis
- [p,q,w,t,u,b,ssqdif] = polypls(x,y,lv,n)
Description
POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation.
Use POLYPRED to make predictions with new data.
Inputs
- x = matrix of predictor variables (X-block),
- y = vector or matrix of the predicted variables (Y-block),
- lv = maximum number of latent variables to consider,
- n = order of polynomial to use for the inner-relation.
Outputs
- p = x-block loadings,
- q = y-block loadings,
- w = x-block weights,
- t = x-block scores,
- u = y-block scores,
- b = matrix of polynomial coefficients for the inner-relation,
- ssqdif = table of x- and y-block variance captured by the PLS model.
Options
options = a structure array with the following fields:
- display: [ 'off' | {'on'} ] governs display of SSQ table