gbm: relative.influence – R documentation

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gbm

relative.influence

Methods for estimating relative influence

Description

Helper functions for computing the relative influence of each variable in the gbm object.

Usage

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)

Arguments

`object`	a `gbm` object created from an initial call to `gbm`.
`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 cross-validation was performed, the number of trees resulting in lowest cross-validation error will be used; otherwise, all trees will be used.
`scale.`	whether or not the result should be scaled. Defaults to `FALSE`.
`sort.`	whether or not the results should be (reverse) sorted. Defaults to `FALSE`.
`y, f, w, offset, dist, baseline`	For `gbm.loss`: These components are the outcome, predicted value, observation weight, offset, distribution, and comparison loss function, respectively.
`group, max.rank`	Used internally when `distribution = \'pairwise\'`.

Details

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.

Value

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.

Author(s)

Greg Ridgeway gregridgeway@gmail.com

References

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.

L. Breiman (2001). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.