| 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); require(graphics)
summary(anscombe)
##-- now some "magic" to do the 4 regressions in a loop:
ff <- y ~ x
mods <- setNames(as.list(1:4), paste0("lm", 1:4))
for(i in 1:4) {
ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
## or ff[[2]] <- as.name(paste0("y", i))
## ff[[3]] <- as.name(paste0("x", i))
mods[[i]] <- lmi <- lm(ff, data= anscombe)
print(anova(lmi))
}
## See how close they are (numerically!)
sapply(mods, coef)
lapply(mods, function(fm) coef(summary(fm)))
## 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(paste0(c("y","x"), i), as.name)
plot(ff, data=anscombe, col="red", pch=21, bg = "orange", cex = 1.2,
xlim=c(3,19), ylim=c(3,13))
abline(mods[[i]], col="blue")
}
mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex=1.5)
par(op)