relative.influence {erboost}

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.

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.

Value

Returns an unprocessed vector of estimated relative influences.

Author(s)

Yi Yang [e-mail] and Hui Zou [e-mail]

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