Learn R Programming

quantreg (version 5.98)

Mammals: Garland(1983) Data on Running Speed of Mammals

Description

Observations on the maximal running speed of mammal species and their body mass.

Usage

data(Mammals)

Arguments

Format

A data frame with 107 observations on the following 4 variables.

weight

Body mass in Kg for "typical adult sizes"

speed

Maximal running speed (fastest sprint velocity on record)

hoppers

logical variable indicating animals that ambulate by hopping, e.g. kangaroos

specials

logical variable indicating special animals with "lifestyles in which speed does not figure as an important factor": Hippopotamus, raccoon (Procyon), badger (Meles), coati (Nasua), skunk (Mephitis), man (Homo), porcupine (Erithizon), oppossum (didelphis), and sloth (Bradypus)

Details

Used by Chappell (1989) and Koenker, Ng and Portnoy (1994) to illustrate the fitting of piecewise linear curves.

References

Koenker, R., P. Ng and S. Portnoy, (1994) Quantile Smoothing Splines'' Biometrika, 81, 673-680.

Chappell, R. (1989) Fitting Bent Lines to Data, with Applications ot Allometry, J. Theo. Biology, 138, 235-256.

See Also

rqss

Examples

Run this code
data(Mammals)
attach(Mammals)
x <- log(weight)
y <- log(speed)
plot(x,y, xlab="Weight in log(Kg)", ylab="Speed in log(Km/hour)",type="n")
points(x[hoppers],y[hoppers],pch = "h", col="red")
points(x[specials],y[specials],pch = "s", col="blue")
others <- (!hoppers & !specials)
points(x[others],y[others], col="black",cex = .75)
fit <- rqss(y ~ qss(x, lambda = 1),tau = .9)
plot(fit)

Run the code above in your browser using DataLab