On this Picostat.com statistics page, you will find information about the Mammals data set which pertains to Garland(1983) Data on Running Speed of Mammals. The Mammals data set is found in the quantreg R package. You can load the Mammals data set in R by issuing the following command at the console data("Mammals"). This will load the data into a variable called Mammals. If R says the Mammals data set is not found, you can try installing the package by issuing this command install.packages("quantreg") 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 Mammals R data set. The size of this file is about 2,041 bytes.
Garland(1983) Data on Running Speed of Mammals
Observations on the maximal running speed of mammal species
and their body mass.
A data frame with 107 observations on the following 4 variables.
Body mass in Kg for "typical adult sizes"
Maximal running speed (fastest sprint velocity on record)
logical variable indicating animals that ambulate
by hopping, e.g. kangaroos
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)
Used by Chappell (1989) and Koenker, Ng and Portnoy (1994) to
illustrate the fitting of piecewise linear curves.
Garland, T. (1983) The relation between maximal running speed and body
mass in terrestrial mammals, J. Zoology, 199, 1557-1570.
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.
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)
Dataset imported from https://www.r-project.org.