relative.influence: Methods for estimating relative influence
In erboost: Nonparametric Multiple Expectile Regression via ER-Boost

Description Usage Arguments Details Value Author(s) References See Also

View source: R/erboost.R

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

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

`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.

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.

Returns an unprocessed vector of estimated relative influences.

Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu

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.

summary.erboost

erboost documentation built on May 1, 2019, 9:22 p.m.