Polypls: Difference between revisions
Jump to navigation
Jump to search
imported>Donal No edit summary |
imported>Donal No edit summary |
||
| Line 10: | Line 10: | ||
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. | ||
| Line 27: | Line 25: | ||
* '''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=== | ||
Revision as of 13:42, 6 March 2013
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:
- plots: [ {'none'} | 'final' ] governs plotting of results, and
- order: positive integer for polynomial order {default = 1}.