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Plot a gg_partial object

Source: R/plot.gg_partial.R
plot.gg_partial.Rd

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 class 'gg_partial'
plot(x, labels = NULL, ...)

Arguments

x

A gg_partial object (output of gg_partial).

labels

Optional variable labels for the facet strips. One of: a named character vector (c(bpd_last = "BP Diastole")); a labelled data frame, whose attr(col, "label") values are read; or a two-column key/label data frame. Variables with no label keep their raw name. Defaults to NULL (raw names).

...

Not currently used; reserved for future arguments.

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").

Details

When a model label was attached in gg_partial(), lines are colored by model, which is 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)