Animals2 R Documentation

## 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")
```