Description Format Details Source References Examples

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.

- x1
altitude (in meters)

- x2
slope (en degrees)

- x3
number of pines in the area

- x4
height (in meters) of the tree sampled at the center of the area

- x5
diameter (in meters) of the tree sampled at the center of the area

- x6
index of the settlement density

- x7
orientation of the area (from 1 if southbound to 2 otherwise)

- x8
height (in meters) of the dominant tree

- x9
number of vegetation strata

- x10
mix settlement index (from 1 if not mixed to 2 if mixed)

- x11
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.

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992),
“La régression, nouveaux regards sur une ancienne
méthode statistique”, INRA,
*Actualités Scientifiques et Agronomiques*, Masson,
Paris.

J.-M. Marin, C. Robert. (2007). *Bayesian Core: A Practical
Approach to Computational Bayesian Statistics*. Springer, New-York, pages
48-49.

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