plot.erboost: Marginal plots of fitted erboost objects

In erboost: Nonparametric Multiple Expectile Regression via ER-Boost

Description

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

Usage

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

Arguments

x	a erboost.object fitted using a call to erboost
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 erboost formula. If length(i.var) is between 1 and 3 then plot.erboost produces the plots. Otherwise, plot.erboost 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.erboost 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.erboost produces low dimensional projections of the erboost.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.erboost 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.erboost produces no graphics and only returns the grid of evaluation points and their average predictions.

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(4).

See Also

erboost, erboost.object, plot

erboost documentation built on May 1, 2019, 9:22 p.m.