fsemipar (version 1.1.1)

predict.fsim: 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 fsim.kernel
predict(object, newdata = NULL, y.test = NULL, ...)
# S3 method for fsim.kNN
predict(object, newdata = NULL, y.test = NULL, ...)

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.

Arguments

object

Output of the fsim.kernel.fit, fsim.kernel.fit.optim, fsim.kNN.fit or fsim.kNN.fit.optim functions (i.e. an object of the class fsim.kernel or fsim.kNN).

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.

Author

German Aneiros Perez

Silvia Novo Diaz

Details

The prediction is computed using the functions fsim.kernel.test and fsim.kernel.fit, respectively.

See Also

fsim.kernel.fit and fsim.kernel.test or fsim.kNN.fit and fsim.kNN.test.

Examples

# \donttest{
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
# }