Pine dataset

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.

Format

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

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)
str(pine)
#> 'data.frame':	33 obs. of  11 variables:
#>  $ x1 : int  1200 1342 1231 1254 1357 1250 1422 1309 1127 1075 ...
#>  $ x2 : int  22 28 28 28 32 27 37 46 24 34 ...
#>  $ x3 : int  1 8 5 18 7 1 22 7 2 9 ...
#>  $ x4 : num  4 4.4 2.4 3 3.7 4.4 3 5.7 3.5 4.3 ...
#>  $ x5 : num  14.8 18 7.8 9.2 10.7 14.8 8.1 19.6 12.6 12 ...
#>  $ x6 : num  1 1.5 1.3 2.3 1.4 1 2.7 1.5 1 1.6 ...
#>  $ x7 : num  1.1 1.5 1.6 1.7 1.7 1.7 1.9 1.3 1.7 1.8 ...
#>  $ x8 : num  5.9 6.4 4.3 6.9 6.6 5.8 8.3 7.8 4.9 6.8 ...
#>  $ x9 : num  1.4 1.7 1.5 2.3 1.8 1.3 2.5 1.8 1.5 2 ...
#>  $ x10: num  1.4 1.7 1.4 1.6 1.3 1.4 2 1.6 2 2 ...
#>  $ x11: num  2.37 1.47 1.13 0.85 0.24 1.49 0.3 0.07 3 1.21 ...