Helper functions for computing the relative influence of each variable in the gbm object.
relative.influence(object, n.trees, scale., sort.)
permutation.test.gbm(object, n.trees)
gbm.loss(y,f,w,offset,dist,baseline, group, max.rank)
a gbm
object created from an initial call to gbm
.
the number of trees to use for computations. If not provided, the the function will guess: if a test set was used in fitting, the number of trees resulting in lowest test set error will be used; otherwise, if cross-validation was performed, the number of trees resulting in lowest cross-validation error will be used; otherwise, all trees will be used.
whether or not the result should be scaled. Defaults to FALSE
.
whether or not the results should be (reverse) sorted.
Defaults to FALSE
.
For gbm.loss
: These components are the
outcome, predicted value, observation weight, offset, distribution, and comparison
loss function, respectively.
Used internally when distribution = \'pairwise\'
.
By default, returns an unprocessed vector of estimated relative influences.
If the scale.
and sort.
arguments are used, returns a processed
version of the same.
This is not intended for end-user use. These functions offer the different
methods for computing the relative influence in summary.gbm
.
gbm.loss
is a helper function for permutation.test.gbm
.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001). Random Forests.