R: Marginal plots of fitted gbm objects

plot.gbm {gbm} | R Documentation

## 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 class '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`.

### 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.

### Value

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

### 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.

### See Also

`partial`, `plotPartial`, `gbm`, and `gbm.object`.

[Package gbm version 2.1.8 Index]