R: Methods for estimating relative influence

relative.influence {gbm} | R Documentation

## 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.

### See Also

`summary.gbm`

[Package gbm version 2.1.8 Index]