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.