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$n.trees,
continuous.resolution = 100,
return.grid = FALSE,
type = c("link", "response"),
level.plot = TRUE,
contour = FALSE,
number = 4,
overlap = 0.1,
col.regions = viridis::viridis,
...
)
If return.grid = TRUE
, a grid of evaluation points and their
average predictions. Otherwise, a plot is returned.
A gbm.object
that was fit using a call to
gbm
.
Vector of indices or the names of the variables to plot. If
using indices, the variables are indexed in the same order that they appear
in the initial gbm
formula. If length(i.var)
is between 1 and
3 then plot.gbm
produces the plots. Otherwise, plot.gbm
returns only the grid of evaluation points and their average predictions
Integer specifying the number of trees to use to generate the
plot. Default is to use x$n.trees
(i.e., the entire ensemble).
Integer specifying the number of equally space points at which to evaluate continuous predictors.
Logical indicating whether or not to produce graphics
FALSE
or only return the grid of evaluation points and their average
predictions TRUE
. This is useful for customizing the graphics for
special variable types, or for higher dimensional graphs.
Character string specifying the type of prediction to plot on the
vertical axis. See predict.gbm
for details.
Logical indicating whether or not to use a false color
level plot (TRUE
) or a 3-D surface (FALSE
). Default is
TRUE
.
Logical indicating whether or not to add contour lines to the
level plot. Only used when level.plot = TRUE
. Default is FALSE
.
Integer specifying the number of conditional intervals to use
for the continuous panel variables. See co.intervals
and equal.count
for further details.
The fraction of overlap of the conditioning variables. See
co.intervals
and equal.count
for further details.
Color vector to be used if level.plot
is
TRUE
. Defaults to the wonderful Matplotlib 'viridis' color map
provided by the viridis
package. See viridis
for details.
Additional optional arguments to be passed onto
plot
.
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).
B. M. Greenwell (2017). "pdp: An R Package for Constructing Partial Dependence Plots," The R Journal 9(1), 421--436. https://journal.r-project.org/archive/2017/RJ-2017-016/index.html.
partial
, plotPartial
,
gbm
, and gbm.object
.