R Dataset / Package robustbase / Animals2

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<iframe src="https://embed.picostat.com/r-dataset-package-robustbase-animals2.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-33388.csv 1.3 KB
Dataset License
GNU General Public License v2.0
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GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the Animals2 data set which pertains to Brain and Body Weights for 65 Species of Land Animals. The Animals2 data set is found in the robustbase R package. You can load the Animals2 data set in R by issuing the following command at the console data("Animals2"). This will load the data into a variable called Animals2. If R says the Animals2 data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Animals2 R data set. The size of this file is about 1,333 bytes.

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")
--

Dataset imported from https://www.r-project.org.

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