bestglm (version 0.13)

zprostate: Prostate cancer data. Standardized.

Description

Data with 8 inputs and one output used to illustrate the prediction problem and regression in the textbook of Hastie, Tibshirani and Freedman (2009).

Usage

data(zprostate)

source

http://www-stat-class.stanford.edu/~tibs/ElemStatLearn/

Details

A study of 97 men with prostate cancer examined the correlation between PSA (prostate specific antigen) and a number of clinical measurements: lcavol, lweight, lbph, svi, lcp, gleason, pgg45

References

Hastie, Tibshirani & Friedman. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Ed. Springer.

Examples

#Example 1. Prostate data. Table 3.3 HTF.
data(zprostate)
#full dataset
trainQ<-zprostate[,10]
train <-zprostate[trainQ,-10]
test <-zprostate[!trainQ,-10]
ans<-lm(lpsa~., data=train)
sig<-summary(ans)$sigma
yHat<-predict(ans, newdata=test)
yTest<-zprostate$lpsa[!trainQ]
TE<-mean((yTest-yHat)^2)
#subset
ansSub<-bestglm(train, IC="BICq")$BestModel
sigSub<-summary(ansSub)$sigma
yHatSub<-predict(ansSub, newdata=test)
TESub<-mean((yTest-yHatSub)^2)
m<-matrix(c(TE,sig,TESub,sigSub), ncol=2)
dimnames(m)<-list(c("TestErr","Sd"),c("LS","Best"))
m