relative.influence: Methods for estimating relative influence
In DexGroves/gbm-lrd: Generalized Boosted Regression Models

Description Usage Arguments Details Value Author(s) References See Also

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

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relative.influence(object, n.trees, scale., sort.)
permutation.test.gbm(object, n.trees)
gbm.loss(y,f,w,offset,dist,baseline, group, max.rank)

`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 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\'`.

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.

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):1189-1232.

L. Breiman (2001). Random Forests.

summary.gbm

DexGroves/gbm-lrd documentation built on May 6, 2019, 1:35 p.m.