summary.erboost {erboost}

Summary of a erboost object

Description

Computes the relative influence of each variable in the erboost object.

Usage

## S3 method for class 'erboost'
summary(object,
        cBars=length(object$var.names),
        n.trees=object$n.trees,
        plotit=TRUE,
        order=TRUE,
        method=relative.influence,
        normalize=TRUE,
        ...)

Arguments

object

a erboost object created from an initial call to erboost.

cBars

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.

n.trees

the number of trees used to generate the plot. Only the first n.trees trees will be used.

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. 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.

normalize

if FALSE then summary.erboost returns the unnormalized influence.

...

other arguments passed to the plot function.

Details

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.

Value

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.

Author(s)

Yi Yang [e-mail] and Hui Zou [e-mail]

References

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.

See Also

erboost