Helper functions for computing the relative influence of each variable in the erboost object.
1 2 3
the number of trees to use for computations.
This is not intended for end-user use. These functions offer the different
methods for computing the relative influence in
erboost.loss is a helper function for
Returns an unprocessed vector of estimated relative influences.
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.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
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