"plot"(x, i.var = 1, n.trees = x$n.trees, continuous.resolution = 100, return.grid = FALSE, ...)erboost.object fitted using a call to erboosterboost 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 predictionsn.trees trees will be usedTRUE 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 return.grid is true then plot.erboost produces no
graphics and only returns the grid of evaluation points and their average
predictions.
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.
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