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 remove from the matrix of
predictors on purpose.
A data frame with 33 observations on the following 10 variables and one missing value.
altitude (in meters)
slope (en degrees)
number of pines in the area
height (in meters) of the tree sampled at the center of the area
diameter (in meters) of the tree sampled at the center of the area
index of the settlement density
orientation of the area (from 1 if southbound to 2 otherwise)
height (in meters) of the dominant tree
number of vegetation strata
mix settlement index (from 1 if not mixed to 2 if mixed)
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.
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 XpineNAX21 is a dataset with a missing
value for testing purpose.
#> 'data.frame': 33 obs. of 10 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 ...