R: Summary of a TDboost object

summary.TDboost {TDboost} | R Documentation

## Summary of a TDboost object

### Description

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

### Usage

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

### Arguments

`object` | a `TDboost` object created from an initial call to `TDboost`.
`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.TDboost`. 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 `TDboost` currently computes using the entire training dataset (not the out-of-bag observations.
`normalize` | if `FALSE` then `summary.TDboost` returns the unnormalized influence.
`...` | other arguments passed to the plot function.

### Details

This returns the reduction attributable to each variable 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 yi.yang6@mcgill.ca, Wei Qian wxqsma@rit.edu and Hui Zou hzou@stat.umn.edu

### References

Yang, Y., Qian, W. and Zou, H. (2013), “A Boosted Tweedie Compound Poisson Model for Insurance Premium” Preprint.

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.

### See Also

`TDboost`

[Package TDboost version 1.2 Index]