Prediction with Bagging

library(ElemStatLearn);data(ozone,package="ElemStatLearn")
ozone<-ozone[order(ozone$ozone),]
head(ozone)

##     ozone radiation temperature wind
## 17      1         8          59  9.7
## 19      4        25          61  9.7
## 14      6        78          57 18.4
## 45      7        48          80 14.3
## 106     7        49          69 10.3
## 7       8        19          61 20.1

We want to predict the temperature with a function of ozone. ### Bagged loess

ll<-matrix(NA,nrow=10,ncol=155)
for(i in 1:10){
  ss<-sample(1:dim(ozone)[1],replace=T)
  ozone0<-ozone[ss,]
  ozone0<-ozone0[order(ozone0$ozone),]
  loess0<-loess(temperature~ozone,data=ozone0,span=0.2)
  ll[i,]<-predict(loess0,newdata=data.frame(ozone=1:155))
}  

We plot the results of the code above.

plot(ozone$ozone,ozone$temperature,pch=10,cex=0.5)
for(i in 1:10){lines(1:155,ll[i,],col="grey",lwd=2)}
lines(1:155,apply(ll,2,mean),col="red",lwd=2)

In the plot above, the black dots represent the original data; the 10 grey lines the loess fits of each resampling; the red line, the average of the 10 grey lines.