relative.influence: Methods for estimating relative influence

In TDboost: A Boosted Tweedie Compound Poisson Model

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 [e-mail], Wei Qian [e-mail] and Hui Zou [e-mail]

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

TDboost documentation built on Sept. 13, 2022, 5:05 p.m.