Computes the relative influence of each variable in the erboost object.
# S3 method for erboost
summary(object,
cBars=length(object$var.names),
n.trees=object$n.trees,
plotit=TRUE,
order=TRUE,
method=relative.influence,
normalize=TRUE,
...)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.
a erboost object created from an initial call to
erboost.
the number of bars to plot. If order=TRUE the only the
variables with the cBars largest relative influence will appear in the
barplot. If order=FALSE then the first cBars variables will
appear in the plot. In either case, the function will return the relative
influence of all of the variables.
the number of trees used to generate the plot. Only the first
n.trees trees will be used.
an indicator as to whether the plot is generated.
an indicator as to whether the plotted and/or returned relative influences are sorted.
The function used to compute the relative influence.
relative.influence is the default and is the same as that
described in Friedman (2001). The other current (and experimental) choice is
permutation.test.erboost. This method randomly permutes each predictor
variable at a time and computes the associated reduction in predictive
performance. This is similar to the variable importance measures Breiman uses
for random forests, but erboost currently computes using the entire training
dataset (not the out-of-bag observations.
if FALSE then summary.erboost returns the
unnormalized influence.
other arguments passed to the plot function.
Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu
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.
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.
erboost