Marginal plots of fitted gbm objects

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, ...)

Arguments

x	A `gbm.object` that was fit using a call to `gbm`.
i.var	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
n.trees	Integer specifying the number of trees to use to generate the plot. Default is to use `x$n.trees` (i.e., the entire ensemble).
continuous.resolution	Integer specifying the number of equally space points at which to evaluate continuous predictors.
return.grid	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.
type	Character string specifying the type of prediction to plot on the vertical axis. See `predict.gbm` for details.
level.plot	Logical indicating whether or not to use a false color level plot (`TRUE`) or a 3-D surface (`FALSE`). Default is `TRUE`.
contour	Logical indicating whether or not to add contour lines to the level plot. Only used when `level.plot = TRUE`. Default is `FALSE`.
number	Integer specifying the number of conditional intervals to use for the continuous panel variables. See `co.intervals` and `equal.count` for further details.
overlap	The fraction of overlap of the conditioning variables. See `co.intervals` and `equal.count` for further details.
col.regions	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`.

Value

If return.grid = TRUE, a grid of evaluation points and their average predictions. Otherwise, a plot is returned.

Details

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.

Note

More flexible plotting is available using the partial and plotPartial functions.

References

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.