plot.TDboost: Marginal plots of fitted TDboost objects

In emeryyi/tdboost: A Boosted Tweedie Compound Poisson Model

Marginal plots of fitted TDboost objects

Description

Plots the marginal effect of the selected variables by "integrating" out the other variables.

Usage

## S3 method for class 'TDboost'
plot(x,
     i.var = 1,
     n.trees = x$n.trees,
     continuous.resolution = 100,
     return.grid = FALSE,
     ...)

Arguments

x	a TDboost.object fitted using a call to TDboost
i.var	a vector of indices or the names of the variables to plot. If using indices, the variables are indexed in the same order that they appear in the initial TDboost formula. If length(i.var) is between 1 and 3 then plot.TDboost produces the plots. Otherwise, plot.TDboost returns only the grid of evaluation points and their average predictions
n.trees	the number of trees used to generate the plot. Only the first n.trees trees will be used
continuous.resolution	The number of equally space points at which to evaluate continuous predictors
return.grid	if TRUE then plot.TDboost produces no graphics and only returns the grid of evaluation points and their average predictions. This is useful for customizing the graphics for special variable types or for dimensions greater than 3
...	other arguments passed to the plot function

Details

plot.TDboost produces low dimensional projections of the TDboost.object by integrating out the variables not included in the i.var argument. The function selects a grid of points and uses the weighted tree traversal method described in Friedman (2001) to do the integration. Based on the variable types included in the projection, plot.TDboost selects an appropriate display choosing amongst line plots, contour plots, and lattice plots. If the default graphics are not sufficient the user may set return.grid=TRUE, store the result of the function, and develop another graphic display more appropriate to the particular example.

Value

Nothing unless return.grid is true then plot.TDboost produces no graphics and only returns the grid of evaluation points and their average predictions.

Author(s)

Yi Yang [e-mail], Wei Qian [e-mail] and Hui Zou [e-mail]

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(4).

See Also

TDboost, TDboost.object, plot

emeryyi/tdboost documentation built on Aug. 16, 2022, 7:17 a.m.