predict.fsim {fsemipar} | R Documentation |
Prediction for FSIM
Description
predict
method for the functional single-index model (FSIM) fitted using fsim.kernel.fit
, fsim.kernel.fit.optim
, fsim.kNN.fit
and fsim.kNN.fit.optim
.
Usage
## S3 method for class 'fsim.kernel'
predict(object, newdata = NULL, y.test = NULL, ...)
## S3 method for class 'fsim.kNN'
predict(object, newdata = NULL, y.test = NULL, ...)
Arguments
object |
Output of the |
newdata |
A matrix containing new observations of the functional covariate collected by row. |
y.test |
(optional) A vector containing the new observations of the response. |
... |
Further arguments passed to or from other methods. |
Details
The prediction is computed using the functions fsim.kernel.test
and fsim.kernel.fit
, respectively.
Value
The function returns the predicted values of the response (y
) for newdata
. If !is.null(y.test)
, it also provides the mean squared error of prediction (MSEP
) computed as mean((y-y.test)^2)
.
If is.null(newdata)
the function returns the fitted values.
Author(s)
German Aneiros Perez german.aneiros@udc.es
Silvia Novo Diaz snovo@est-econ.uc3m.es
See Also
fsim.kernel.fit
and fsim.kernel.test
or fsim.kNN.fit
and fsim.kNN.test
.
Examples
data(Tecator)
y<-Tecator$fat
X<-Tecator$absor.spectra2
train<-1:160
test<-161:215
#FSIM fit.
fit.kernel<-fsim.kernel.fit(y[train],x=X[train,],max.q.h=0.35, nknot=20,
range.grid=c(850,1050),nknot.theta=4)
fit.kNN<-fsim.kNN.fit(y=y[train],x=X[train,],max.knn=20,nknot=20,
nknot.theta=4, range.grid=c(850,1050))
test<-161:215
pred.kernel<-predict(fit.kernel,newdata=X[test,],y.test=y[test])
pred.kernel$MSEP
pred.kNN<-predict(fit.kNN,newdata=X[test,],y.test=y[test])
pred.kNN$MSEP