Marginal plots of fitted gbm objects
Plots the marginal effect of the selected variables by "integrating" out the other variables.
plot.gbm(x, i.var = 1, n.trees = x$n.trees, continuous.resolution = 100, return.grid = FALSE, ...)
gbm.objectfitted using a call to
- a vector of indices of the variables to plot. The variables
are indexed in the same order that they appear in the initial
length(i.var) is between 1 and 3 then
plot.gbmproduces the plots. Otherwise,
- the number of trees used to generate the plot. Only the first
n.treestrees will be used.
- The number of equally space points at which to evaluate continuous predictors
- if true then
plot.gbmproduces 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.
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
- Nothing unless
return.gridis true then
plot.gbmproduces no graphics and only returns the grid of evaluation points and their average predictions.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(4).