caterpillar: Pine processionary caterpillar dataset

Description Usage Format Details Source Examples

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

1

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

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.

Source

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

Examples

1
2

Example output

Loading required package: MASS
Loading required package: mnormt
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: combinat

Attaching package: 'combinat'

The following object is masked from 'package:utils':

    combn

       x1             x2              x3              x4              x5       
 Min.   :1075   Min.   :15.00   Min.   : 0.00   Min.   :2.400   Min.   :1.100  
 1st Qu.:1228   1st Qu.:24.00   1st Qu.: 4.00   1st Qu.:3.700   1st Qu.:1.600  
 Median :1309   Median :28.00   Median : 8.00   Median :4.400   Median :1.700  
 Mean   :1315   Mean   :29.03   Mean   :11.45   Mean   :4.452   Mean   :1.658  
 3rd Qu.:1396   3rd Qu.:34.00   3rd Qu.:18.00   3rd Qu.:5.300   3rd Qu.:1.800  
 Max.   :1575   Max.   :46.00   Max.   :32.00   Max.   :6.500   Max.   :1.900  
       x6               x7              x8              y         
 Min.   : 3.600   Min.   :1.100   Min.   :1.300   Min.   :0.0300  
 1st Qu.: 5.900   1st Qu.:1.500   1st Qu.:1.600   1st Qu.:0.1800  
 Median : 7.200   Median :2.000   Median :1.800   Median :0.6700  
 Mean   : 7.539   Mean   :1.982   Mean   :1.752   Mean   :0.8112  
 3rd Qu.: 9.100   3rd Qu.:2.500   3rd Qu.:2.000   3rd Qu.:1.1300  
 Max.   :13.700   Max.   :2.900   Max.   :2.000   Max.   :3.0000  

bayess documentation built on May 29, 2017, 9:39 p.m.

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