Observations on the maximal running speed of mammal species
and their body mass.
Usage
data(Mammals)
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.
Source
Garland, T. (1983) The relation between maximal running speed and body
mass in terrestrial mammals, J. Zoology, 199, 1557-1570.
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
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)