Brain and Body Weights for 65 Species of Land Animals
A data frame with average brain and body weights for 62 species of land mammals and three others.
Animals2
bodybody weight in kg
brainbrain weight in g
After loading the MASS package, the data set is simply constructed by
Animals2 <- local({D <- rbind(Animals, mammals);
unique(D[order(D$body,D$brain),])}).
Rousseeuw and Leroy (1987)'s ‘brain’ data is the same as
MASS's Animals (with Rat and Brachiosaurus interchanged,
see the example below).
Weisberg, S. (1985) Applied Linear Regression. 2nd edition. Wiley, pp. 144–5.
P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley, p. 57.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Forth Edition. Springer.
data(Animals2)
## Sensible Plot needs doubly logarithmic scale
plot(Animals2, log = "xy")
## Regression example plot:
plotbb <- function(bbdat) {
d.name <- deparse(substitute(bbdat))
plot(log(brain) ~ log(body), data = bbdat, main = d.name)
abline( lm(log(brain) ~ log(body), data = bbdat))
abline(MASS::rlm(log(brain) ~ log(body), data = bbdat), col = 2)
legend("bottomright", leg = c("lm", "rlm"), col=1:2, lwd=1, inset = 1/20)
}
plotbb(bbdat = Animals2)
## The `same' plot for Rousseeuw's subset:
data(Animals, package = "MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
plotbb(bbdat = brain)
lbrain <- log(brain)
plot(mahalanobis(lbrain, colMeans(lbrain), var(lbrain)),
main = "Classical Mahalanobis Distances")
mcd <- covMcd(lbrain)
plot(mahalanobis(lbrain,mcd$center,mcd$cov),
main = "Robust (MCD) Mahalanobis Distances")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.