# Prediction for linear models `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`. ```r ## 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. ## Returns 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 german.aneiros@udc.es Silvia Novo Diaz snovo@est-econ.uc3m.es ## See Also `lm.pels.fit` and `PVS.fit`. ## Examples ```r 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]) ```

predict.lm function