Plots the marginal effect of the selected variables by "integrating" out the other variables.
# S3 method for GBMFit
plot(
x,
var_index = 1,
num_trees = gbm_fit_obj$params$num_trees,
continuous_resolution = 100,
grid_levels = NULL,
return_grid = FALSE,
type = "link",
...
)Nothing unless return_grid is true then
plot.GBMFit produces no graphics and only returns the grid of
evaluation points and their average predictions.
a GBMFit object fitted using a call to
gbmt
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 gbmt formula. If
length(var_index) is between 1 and 3 then plot.GBMFit
produces the plots. Otherwise, plot.GBMFit returns only the
grid of evaluation points and their average predictions
the number of trees used to generate the
plot. Only the first num_trees trees will be used
The number of equally space points at which to evaluate continuous predictors
A list containing the points at which to
evaluate each predictor in var_index (in the same order as
var_index). 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(var_index) 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
if TRUE then plot.GBMFit 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
the type of prediction to plot on the vertical
axis. See predict_gmt
other arguments passed to the plot function
plot.GBMFit produces low dimensional projections of the
GBMFit object, see gbmt, by integrating out
the variables not included in the var_index 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_gbmt 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).
gbmt, plot