Plots the marginal effect of the selected variables by "integrating" out the other variables.
# S3 method for gbm
plot(x,
i.var = 1,
n.trees = x$n.trees,
continuous.resolution = 100,
return.grid = FALSE,
type = "link",
...)a gbm.object fitted using a call to gbm
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 gbm formula.
If length(i.var) is between 1 and 3 then plot.gbm produces the plots. Otherwise,
plot.gbm 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.gbm 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
the type of prediction to plot on the vertical axis. See
predict.gbm
other arguments passed to the plot function
Nothing unless return.grid is true then plot.gbm produces no
graphics and only returns the grid of evaluation points and their average
predictions.
plot.gbm produces low dimensional projections of the
gbm.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.gbm 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.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(4).