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gbm (version 2.2.2)

plot.gbm: Marginal plots of fitted gbm objects

Description

Plots the marginal effect of the selected variables by "integrating" out the other variables.

Usage

# 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,
  ...
)

Value

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

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.

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.

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.

See Also

partial, plotPartial, gbm, and gbm.object.