10001036

##----------
## This is a trivially updated version based on the R document "?anscombe".
require(stats); require(graphics)
anscombe

##-- now some "magic" to do the 4 regressions in a loop:##< -
ff = y ~ x
for(i in 1:4) {
ff[2:3] = lapply(paste(c(”y”,”x”), i, sep=”"), as.name)
assign(paste(”lm.”,i,sep=”"), lmi <- lm(ff, data= anscombe))
}

## See how close they are (numerically!)
sapply(objects(pattern=”lm\\.[1-4]$”), function(n) coef(get(n)))
lapply(objects(pattern=”lm\\.[1-4]$”),
function(n) coef(summary(get(n))))

## Now, do what you should have done in the first place: PLOTS
op <- par(mfrow=c(4,3),mar=.1+c(4,4,1,1), oma= c(0,0,2,0))
for(i in 1:4) {
ff[2:3] <- lapply(paste(c(”y”,”x”), i, sep=”"), as.name)
plot(ff, data =anscombe, col=”red”, pch=21, bg = “orange”, cex = 1.2,
xlim=c(3,19), ylim=c(3,13))
abline(get(paste(”lm.”,i,sep=”")), col=”blue”)
plot(lm(ff, data =anscombe),which=1,col=”red”, pch=21, bg = “orange”, cex = 1.2
,sub.caption=”",caption=”" )
plot(lm(ff, data =anscombe),which=2,col=”red”, pch=21, bg = “orange”, cex = 1.2
,sub.caption=”",caption=”" )
}
mtext(”Anscombe’s 4 Regression data sets”, outer = TRUE, cex=1.5)
par(op)

##

## Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27, 17–21.