bayess (version 1.4)

caterpillar: Pine processionary caterpillar dataset

Description

The caterpillar dataset is extracted from a 1973 study on pine processionary caterpillars. The response variable is the log transform of the number of nests per unit. There are $p=8$ potential explanatory variables and $n=33$ areas.

Usage

data(caterpillar)

Format

A data frame with 33 observations on the following 9 variables.

x1

altitude (in meters)

x2

slope (in degrees)

x3

number of pine trees in the area

x4

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

x5

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

x6

height (in meters) of the dominant tree

x7

number of vegetation strata

x8

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

y

logarithmic transform of the average number of nests of caterpillars per tree

Source

Tomassone, R., Dervin, C., and Masson, J.P. (1993) Biometrie: modelisation de phenomenes biologiques. Dunod, Paris.

Details

This dataset is used in Chapter 3 on linear regression. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. It was first published and studied in Tomassone et al. (1993). The response variable is the logarithmic transform of the average number of nests of caterpillars per tree in an area of 500 square meters (which corresponds to the last column in caterpillar). There are $p=8$ potential explanatory variables defined on $n=33$ areas.

Examples

data(caterpillar)
summary(caterpillar)