ggRandomForests (version 3.1.2)

plot.gg_partial: Plot a gg_partial object

Description

Turns a gg_partial object into a ggplot2 figure. Each curve is a partial dependence trace -- the forest's average prediction as one predictor is swept across its range while the rest are marginalized over the training data. Continuous predictors appear as line plots; categorical predictors appear as bar charts. Both panels are faceted by variable name so you can compare the shape and scale of each variable's effect at a glance.

Usage

# S3 method for gg_partial
plot(x, ...)

Value

A ggplot (or patchwork) object. When only one variable type is present a single ggplot is returned. When both continuous and categorical variables are present the two panels are combined vertically via patchwork::wrap_plots(), which also satisfies inherits(p, "ggplot").

Arguments

x

A gg_partial object (output of gg_partial).

...

Not currently used; reserved for future arguments.

Details

When a model label was attached in gg_partial(), lines are coloured by model -- handy for overlaying results from two forests (e.g., one tuned, one default) in the same figure.

See Also

gg_partial, plot.gg_variable

Examples

set.seed(42)
airq <- na.omit(airquality)
rf <- randomForestSRC::rfsrc(Ozone ~ ., data = airq, ntree = 50)
pv <- randomForestSRC::plot.variable(rf, partial = TRUE, show.plots = FALSE)
pd <- gg_partial(pv)
plot(pd)