Description Usage Arguments Details Value Author(s) References See Also
Computes the relative influence of each variable in the erboost object.
1 2 3 4 5 6 7 8 9 
object 
a 
cBars 
the number of bars to plot. If 
n.trees 
the number of trees used to generate the plot. Only the first

plotit 
an indicator as to whether the plot is generated. 
order 
an indicator as to whether the plotted and/or returned relative influences are sorted. 
method 
The function used to compute the relative influence.

normalize 
if 
... 
other arguments passed to the plot function. 
This returns the reduction attributeable to each varaible in sum of squared error in predicting the gradient on each iteration. It describes the relative influence of each variable in reducing the loss function. See the references below for exact details on the computation.
Returns a data frame where the first component is the variable name and the second is the computed relative influence, normalized to sum to 100.
Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu
Yang, Y. and Zou, H. (2015), “Nonparametric Multiple Expectile Regression via ERBoost,” Journal of Statistical Computation and Simulation, 84(1), 8495.
G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172181.
https://cran.rproject.org/package=gbm
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):11891232.
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