plot.gbm: Marginal plots of fitted gbm objects
In DexGroves/gbm-lrd: Generalized Boosted Regression Models

Description Usage Arguments Details Value Author(s) References See Also

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

## S3 method for class 'gbm'
plot(x,
     i.var = 1,
     n.trees = x$n.trees,
     continuous.resolution = 100,
     grid.levels = NULL,
     return.grid = FALSE,
     type = "link",
     ...)

`x`	a `gbm.object` fitted using a call to `gbm`
`i.var`	a 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`	the number of trees used to generate the plot. Only the first `n.trees` trees will be used
`continuous.resolution`	The number of equally space points at which to evaluate continuous predictors
`grid.levels`	A list containing the points at which to evaluate each predictor in `i.var` (in the same order as `i.var`). For continuous predictors this is usually a regular sequence of values within the range of the variable. For categorical predictors, the points are the levels of the factor. When `length(i.var)` is one, the values can be provided directly, outside a list. This is NULL by default and generated automatically from the data, using `continuous.resolution` for continuous predictors. Forcing the values can be useful to evaluate two models on the same exact range
`return.grid`	if `TRUE` then `plot.gbm` produces 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
`type`	the type of prediction to plot on the vertical axis. See `predict.gbm`
`...`	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 particular example.

Nothing unless return.grid is true then plot.gbm produces no graphics and only returns the grid of evaluation points and their average predictions.

Greg Ridgeway gregridgeway@gmail.com

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(4).

gbm, gbm.object, plot

DexGroves/gbm-lrd documentation built on May 6, 2019, 1:35 p.m.