Helper functions for computing the relative influence of each variable in the erboost object.
relative.influence(object, n.trees)
permutation.test.erboost(object, n.trees)
erboost.loss(y,f,w,offset,dist,baseline)Returns an unprocessed vector of estimated relative influences.
a erboost object created from an initial call to erboost.
the number of trees to use for computations.
For erboost.loss: These components are the
outcome, predicted value, observation weight, offset, distribution, and comparison
loss function, respectively.
Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu
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.
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