erboost (version 1.0)

relative.influence: Methods for estimating relative influence

Description

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

Usage

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

Arguments

object

a erboost object created from an initial call to erboost.

n.trees

the number of trees to use for computations.

y,f,w,offset,dist,baseline

For erboost.loss: These components are the outcome, predicted value, observation weight, offset, distribution, and comparison loss function, respectively.

Value

• Returns an unprocessed vector of estimated relative influences.

Details

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

References

Y. Yang and H. Zou (2012) Nonparametric Multivariate Expectile Regression via ER-Boost, submitted to Journal of Business & Economic Statistics.

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

L. Breiman (2001). "Random Forests," Available at ftp://ftp.stat.berkeley.edu/pub/users/breiman/randomforest2001.pdf.

See Also

summary.erboost