predict.lm: Prediction for linear models

Description

predict method for:

Linear model (LM) fitted using lm.pels.fit.
Linear model with covariates derived from the discretization of a curve fitted using PVS.fit.

Usage

# S3 method for lm.pels
predict(object, newdata = NULL, y.test = NULL, ...)
# S3 method for PVS
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), then the function returns the fitted values.

Arguments

object: Output of the lm.pels.fit or PVS.fit functions (i.e. an object of the class lm.pels or PVS)
newdata: Matrix containing the new observations of the scalar covariates (LM), or the scalar covariates resulting from the discretisation of a curve. Observations are 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 german.aneiros@udc.es

Silvia Novo Diaz snovo@est-econ.uc3m.es

Examples

Run this code


data("Tecator")
y<-Tecator$fat
z1<-Tecator$protein       
z2<-Tecator$moisture

#Quadratic, cubic and interaction effects of the scalar covariates.
z.com<-cbind(z1,z2,z1^2,z2^2,z1^3,z2^3,z1*z2)
train<-1:160
test<-161:215

#LM fit. 
fit<-lm.pels.fit(z=z.com[train,], y=y[train],lambda.min.l=0.01,
      factor.pn=2, max.iter=5000, criterion="BIC")

#Predictions
predict(fit,newdata=z.com[test,],y.test=y[test])


data(Sugar)

y<-Sugar$ash
z<-Sugar$wave.240

#Outliers
index.y.25 <- y > 25
index.atip <- index.y.25
(1:268)[index.atip]


#Dataset to model
z.sug<- z[!index.atip,]
y.sug <- y[!index.atip]

train<-1:216
test<-217:266

#Fit
fit.pvs<-PVS.fit(z=z.sug[train,], y=y.sug[train],train.1=1:108,train.2=109:216,
          lambda.min.h=0.2,criterion="BIC", max.iter=5000)


#Predictions
predict(fit.pvs,newdata=z.sug[test,],y.test=y.sug[test])