predict.lm {fsemipar}

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 class 'lm.pels'
predict(object, newdata = NULL, y.test = NULL, ...)
## S3 method for class 'PVS'
predict(object, newdata = NULL, y.test = NULL, ...)

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.

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.

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

See Also

lm.pels.fit and PVS.fit.

Examples

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])