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\name{Animals2}
\alias{Animals2}
\title{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 \code{\link[MASS]{Animals}}
and \code{\link[MASS]{mammals}}.
}
\usage{
Animals2
}
\format{
\describe{
\item{\code{body}}{body weight in kg}
\item{\code{brain}}{brain weight in g}
}
}
\source{
Weisberg, S. (1985)
\emph{Applied Linear Regression.}
2nd edition.
Wiley, pp. 144--5.
P. J. Rousseeuw and A. M. Leroy (1987)
\emph{Robust Regression and Outlier Detection.}
Wiley, p. 57.
}
\references{
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Forth Edition. Springer.
}
\note{
After loading the \CRANpkg{MASS} package, the data set is simply constructed by
\code{Animals2 <- local({D <- rbind(Animals, mammals);
unique(D[order(D$body,D$brain),])})}.
Rousseeuw and Leroy (1987)'s \sQuote{brain} data is the same as
\CRANpkg{MASS}'s \code{Animals} (with Rat and Brachiosaurus interchanged,
see the example below).
}
\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")
}
\keyword{datasets}
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