Polypred: Difference between revisions
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imported>Jeremy (Importing text file) |
imported>Jeremy (Importing text file) |
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===Purpose=== | ===Purpose=== | ||
Make predictions for partial least squares regression models with polynomial inner relations. | Make predictions for partial least squares regression models with polynomial inner relations. | ||
===Synopsis=== | ===Synopsis=== | ||
:ypred = polypred(x,b,p,q,w,lv) | :ypred = polypred(x,b,p,q,w,lv) | ||
===Description=== | ===Description=== | ||
POLYPRED uses parameters created by the routine POLYPLS to make predictions from a new x-block matrix of predictor variables x. Inputs are b a matrix of polynomial coefficients for the inner relationship, p the x-block latent variable loadings, q the y-block variable loadings, w the x-block latent variable weights, and the number of latent variables lv. | POLYPRED uses parameters created by the routine POLYPLS to make predictions from a new x-block matrix of predictor variables x. Inputs are b a matrix of polynomial coefficients for the inner relationship, p the x-block latent variable loadings, q the y-block variable loadings, w the x-block latent variable weights, and the number of latent variables lv. | ||
Note: It is important that the scaling of the new data x is the same as that used to create the model parameters in POLYPLS. | Note: It is important that the scaling of the new data x is the same as that used to create the model parameters in POLYPLS. | ||
===See Also=== | ===See Also=== | ||
[[lwrxy]], [[polypls]], [[pls]] | [[lwrxy]], [[polypls]], [[pls]] | ||
Revision as of 15:26, 3 September 2008
Purpose
Make predictions for partial least squares regression models with polynomial inner relations.
Synopsis
- ypred = polypred(x,b,p,q,w,lv)
Description
POLYPRED uses parameters created by the routine POLYPLS to make predictions from a new x-block matrix of predictor variables x. Inputs are b a matrix of polynomial coefficients for the inner relationship, p the x-block latent variable loadings, q the y-block variable loadings, w the x-block latent variable weights, and the number of latent variables lv.
Note: It is important that the scaling of the new data x is the same as that used to create the model parameters in POLYPLS.