This is the complete caterpillar dataset from a 1973 study on pine_full
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=55 areas.
Format
A data frame with 55 observations on the following 11 variables.
- x1
altitude (in meters)
- x2
slope (en degrees)
- x3
number of pine_fulls 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.
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_full)
str(pine_full)
#> 'data.frame': 58 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 ...