R: Methods for estimating relative influence

relative.influence {TDboost} | R Documentation

## Methods for estimating relative influence

### Description

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

### Usage

    relative.influence(object, n.trees)
    permutation.test.TDboost(object, n.trees)
    TDboost.loss(y,f,w,offset,dist,baseline)

### Arguments

`object` | a `TDboost` object created from an initial call to `TDboost`.
`n.trees` | the number of trees to use for computations.
`y,f,w,offset,dist,baseline` | For `TDboost.loss`: These components are the outcome, predicted value, observation weight, offset, distribution, and comparison loss function, respectively.

### Details

This is not intended for end-user use. These functions offer the different methods for computing the relative influence in `summary.TDboost`. `TDboost.loss` is a helper function for `permutation.test.TDboost`.

### Value

Returns an unprocessed vector of estimated relative influences.

### Author(s)

Yi Yang yi.yang6@mcgill.ca, Wei Qian wxqsma@rit.edu and Hui Zou hzou@stat.umn.edu

### References

Yang, Y., Qian, W. and Zou, H. (2013), “A Boosted Tweedie Compound Poisson Model for Insurance Premium” Preprint.

G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172-181.

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

### See Also

`summary.TDboost`

[Package TDboost version 1.2 Index]