erboost (version 1.3)

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

Yang, Y. and Zou, H. (2015), “Nonparametric Multiple Expectile Regression via ER-Boost,” Journal of Statistical Computation and Simulation, 84(1), 84-95.

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

https://cran.r-project.org/package=gbm

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

See Also

summary.erboost