Description Usage Arguments Details Value Author(s) References See Also
View source: R/relative.influence.R
Helper functions for computing the relative influence of each variable in the gbm object.
1 2 3 4 5  relative.influence(object, n.trees, scale. = FALSE, sort. = FALSE)
permutation.test.gbm(object, n.trees)
gbm.loss(y, f, w, offset, dist, baseline, group = NULL, max.rank = NULL)

object 
a 
n.trees 
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 crossvalidation was performed, the number of trees resulting in lowest crossvalidation error will be used; otherwise, all trees will be used. 
scale. 
whether or not the result should be scaled. Defaults to

sort. 
whether or not the results should be (reverse) sorted.
Defaults to 
y, f, w, offset, dist, baseline 
For 
group, max.rank 
Used internally when 
This is not intended for enduser 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
.
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.
Greg Ridgeway gregridgeway@gmail.com
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):11891232.
L. Breiman (2001). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.
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