R: Marginal plots of fitted erboost objects

plot.erboost {erboost}

R Documentation

Marginal plots of fitted erboost objects

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 yiyang@umn.edu and Hui Zou hzou@stat.umn.edu

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).