Complete Pine dataset

This is a supplementary dataset (used as a test set for the pine dataset) that was extracted from a 1973 study on pine_sup 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=22 areas.

Format

A data frame with 22 observations on the following 11 variables.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pine_sups 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

Source

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.

Details

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.

References

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

Examples

data(pine_sup)
str(pine_sup)
#> 'data.frame':	25 obs. of  11 variables:
#>  $ x1 : int  1107 1116 1174 1131 1150 1132 1258 1114 1177 1146 ...
#>  $ x2 : int  31 34 32 30 34 22 14 26 36 26 ...
#>  $ x3 : int  23 6 22 6 12 18 0 9 3 18 ...
#>  $ x4 : num  6 2.5 3.9 4.7 3.1 7 3.8 3.4 4 4.3 ...
#>  $ x5 : num  22.2 6.6 11.9 15.3 9.4 37 8.5 9.6 14.2 17.9 ...
#>  $ x6 : num  2.6 1.3 2.3 1.5 1.7 2.5 1 1.6 1.2 2.3 ...
#>  $ x7 : num  1 1.8 1.7 1.5 1.8 1.5 1.2 1.6 1.3 1.6 ...
#>  $ x8 : num  9 3.9 6.1 6.5 4.8 9 5.6 5.1 5.9 7.7 ...
#>  $ x9 : num  3 1.2 1.8 1.4 1.6 2 1 1.5 1.3 2 ...
#>  $ x10: num  1.4 1.5 1.5 1.3 1.3 1.5 1 1.3 1.6 1.4 ...
#>  $ x11: num  1.17 0.67 0.9 2.32 3.89 6 3.18 0.9 2.5 2 ...