library(faraway) data(state) statedata <- data.frame(state.x77,row.names=state.abb) lmod <- lm(Life.Exp ~ ., statedata) sumary(lmod) lmod <- update(lmod, . ~ . - Area) sumary(lmod) lmod <- update(lmod, . ~ . - Illiteracy) sumary(lmod) lmod <- update(lmod, . ~ . - Income) sumary(lmod) lmod <- update(lmod, . ~ . - Population) sumary(lmod) sumary(lm(Life.Exp ~ Illiteracy+Murder+Frost, statedata)) require(leaps) b <- regsubsets(Life.Exp~.,data=statedata) rs <- summary(b) rs$which AIC <- 50*log(rs$rss/50) + (2:8)*2 plot(AIC ~ I(1:7), ylab="AIC", xlab="Number of Predictors") plot(2:8,rs$adjr2,xlab="No. of Parameters",ylab="Adjusted R-square") which.max(rs$adjr2) plot(2:8,rs$cp,xlab="No. of Parameters",ylab="Cp Statistic") abline(0,1) lmod <- lm(Life.Exp ~ ., data=statedata) step(lmod) h <- lm.influence(lmod)$hat names(h) <- state.abb rev(sort(h)) b<-regsubsets(Life.Exp~.,data=statedata, subset=(state.abb!="AK")) rs <- summary(b) rs$which[which.max(rs$adjr),] stripchart(data.frame(scale(statedata)), method ="jitter", las=2, vertical=TRUE) b<-regsubsets(Life.Exp ~ log(Population)+Income+Illiteracy+ Murder+HS.Grad+Frost+log(Area),statedata) rs <- summary(b) rs$which[which.max(rs$adjr),]