Plots the marginal effect of the selected variables by "integrating" out the other variables.
# S3 method for erboost
plot(x,
i.var = 1,
n.trees = x$n.trees,
continuous.resolution = 100,
return.grid = FALSE,
...)Nothing unless return.grid is true then plot.erboost produces no
graphics and only returns the grid of evaluation points and their average
predictions.
a erboost.object fitted using a call to erboost
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
the number of trees used to generate the plot. Only the first
n.trees trees will be used
The number of equally space points at which to evaluate continuous predictors
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
Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu
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.
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).
erboost, erboost.object, plot