Plots the marginal effect of the selected variables by "integrating" out the other variables.
1 2 3 4 5 6 7 8 
x 
a 
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 
n.trees 
the number of trees used to generate the plot. Only the first

continuous.resolution 
The number of equally space points at which to evaluate continuous predictors 
return.grid 
if 
type 
the type of prediction to plot on the vertical axis. See

... 
other arguments passed to the plot function 
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.
Nothing unless return.grid
is true then plot.gbm
produces no
graphics and only returns the grid of evaluation points and their average
predictions.
Greg Ridgeway gregridgeway@gmail.com
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(4).
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.