anscombe {datasets} | R Documentation |
Four x-y datasets which have the same traditional statistical properties (mean, variance, correlation, regression line, etc.), yet are quite different.
anscombe
A data frame with 11 observations on 8 variables.
x1 == x2 == x3 | the integers 4:14, specially arranged |
x4 | values 8 and 19 |
y1, y2, y3, y4 | numbers in (3, 12.5) with mean 7.5 and sdev 2.03 |
Tufte, Edward R. (1989) The Visual Display of Quantitative Information, 13–14. Graphics Press.
Anscombe, Francis J. (1973) Graphs in statistical analysis. American Statistician, 27, 17–21.
require(stats) summary(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) ## or ff[[2]] <- as.name(paste("y", i, sep="")) ## ff[[3]] <- as.name(paste("x", i, sep="")) assign(paste("lm.",i,sep=""), lmi <- lm(ff, data= anscombe)) print(anova(lmi)) } ## See how close they are (numerically!) sapply(objects(pat="lm\.[1-4]$"), function(n) coef(get(n))) lapply(objects(pat="lm\.[1-4]$"), function(n) summary(get(n))$coef) ## Now, do what you should have done in the first place: PLOTS op <- par(mfrow=c(2,2), 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") } mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex=1.5) par(op)