Lwrxy: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Predictions based on locally weighted regression with y-distance weighting. | Predictions based on locally weighted regression with y-distance weighting. | ||
===Synopsis=== | ===Synopsis=== | ||
:ypred = lwrxy(xnew,xold,yold,lvs,npts,alpha,iter,''out'') | :ypred = lwrxy(xnew,xold,yold,lvs,npts,alpha,iter,''out'') | ||
===Description=== | ===Description=== | ||
NOTE: LWRXY is depreciated. Y-distance weighting should be accessed via the .alpha option of LWRPRED. | NOTE: LWRXY is depreciated. Y-distance weighting should be accessed via the .alpha option of LWRPRED. | ||
LWRXY makes new sample predictions ypred for a new matrix of independent variables xnew based on an existing data set of independent variables xold, and a vector of dependent variables yold. Predictions are made using a locally weighted regression model defined by the number principal components used to model the independent variables lvs, the number of points defined as local npts, the weighting given to the distance in y alpha, and the number of iterations to use iter. | LWRXY makes new sample predictions ypred for a new matrix of independent variables xnew based on an existing data set of independent variables xold, and a vector of dependent variables yold. Predictions are made using a locally weighted regression model defined by the number principal components used to model the independent variables lvs, the number of points defined as local npts, the weighting given to the distance in y alpha, and the number of iterations to use iter. | ||
Optional input ''out'' suppresses printing of the results when set to 0 {default = 1}. | Optional input ''out'' suppresses printing of the results when set to 0 {default = 1}. | ||
Note: Be sure to use the same scaling on new and old samples ''i.e.'' xnew must be scaled the same as xold! | Note: Be sure to use the same scaling on new and old samples ''i.e.'' xnew must be scaled the same as xold! | ||
===See Also=== | ===See Also=== | ||
[[lwpred]], [[pls]], [[polypls]] | [[lwpred]], [[pls]], [[polypls]] | ||
Latest revision as of 14:25, 3 September 2008
Purpose
Predictions based on locally weighted regression with y-distance weighting.
Synopsis
- ypred = lwrxy(xnew,xold,yold,lvs,npts,alpha,iter,out)
Description
NOTE: LWRXY is depreciated. Y-distance weighting should be accessed via the .alpha option of LWRPRED.
LWRXY makes new sample predictions ypred for a new matrix of independent variables xnew based on an existing data set of independent variables xold, and a vector of dependent variables yold. Predictions are made using a locally weighted regression model defined by the number principal components used to model the independent variables lvs, the number of points defined as local npts, the weighting given to the distance in y alpha, and the number of iterations to use iter.
Optional input out suppresses printing of the results when set to 0 {default = 1}.
Note: Be sure to use the same scaling on new and old samples i.e. xnew must be scaled the same as xold!