The caterpillar dataset was extracted from a 1973 study on pine
processionary caterpillars. It assesses the influence of some forest
settlement characteristics on the development of caterpillar colonies. The
response variable is the logarithmic transform of the average number of
nests of caterpillars per tree in an area of 500 square meters (x11).
There are k=10 potentially explanatory variables defined on n=33 areas.
A data frame with 33 observations on the following 11 variables.
altitude (in meters)
slope (en degrees)
number of pines in the area
height (in meters) of the tree sampled at the center of the area
diameter (in meters) of the tree sampled at the center of the area
index of the settlement density
orientation of the area (from 1 if southbound to 2 otherwise)
height (in meters) of the dominant tree
number of vegetation strata
mix settlement index (from 1 if not mixed to 2 if mixed)
logarithmic transform of the average number of nests of caterpillars per tree
These caterpillars got their names from their habit of moving over the
ground in incredibly long head-to-tail processions when leaving their nest
to create a new colony.
The pine_sup dataset can be used as a test set to assess model
prediction error of a model trained on the pine dataset.
J.-M. Marin, C. Robert. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New-York, pages 48-49.