Animals2 {robustbase} | 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")

[Package robustbase version 0.92-8 Index]