Brain and Body Weights for 65 Species of Land Animals

Description

A data frame with average brain and body weights for 62 species of land mammals and three others.

Note that this is simply the union of Animals and mammals.

Usage

Animals2

Format

body

body weight in kg

brain

brain weight in g

Note

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).

Source

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.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Forth Edition. Springer.

Examples

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")