Incomplete dataset from the pine caterpillars example

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. There are k=10 potentially explanatory variables defined on n=33 areas.
The value of x2 for the first observation was removed from the matrix of predictors on purpose.

Format

A data frame with 33 observations on the following 11 variables and one missing value.

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 pineNAX21 is a dataset with a missing value for testing purpose.

Examples

data(pineNAX21)
str(pineNAX21)
#> 'data.frame':	33 obs. of  11 variables:
#>  $ x1 : int  1200 1342 1231 1254 1357 1250 1422 1309 1127 1075 ...
#>  $ x2 : int  NA 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 ...