Split partial dependence data into continuous or categorical datasets

Takes the list returned by rfsrc::plot.variable(partial = TRUE) and separates the variables into two data frames: one for continuous predictors and one for categorical (factor-like) predictors. The split is controlled by cat_limit: variables with more unique x-values than this threshold are treated as continuous; all others are categorical.

Usage

gg_partial(part_dta, nvars = NULL, cat_limit = 10, model = NULL)

Arguments

part_dta

partial plot data from rfsrc::plot.variable

nvars

how many of the partial plot variables to calculate

cat_limit

Categorical features are built when there are fewer than cat_limit unique feature values.

model

a label name applied to all features. Useful when combining multiple partial plot objects in figures.

Value

A named list with two elements:

continuous

data.frame with columns x, yhat, name (and optionally model) for continuous variables

categorical

data.frame with the same columns but with x as a factor, for low-cardinality / categorical variables

See also

gg_partial_rfsrc gg_partialpro

Examples

## Build a small regression forest on the airquality dataset
set.seed(42)
airq <- na.omit(airquality)
rf <- rfsrc(Ozone ~ ., data = airq, ntree = 50)

## Compute partial dependence via plot.variable (show.plots = FALSE to
## suppress the base-graphics output — we only want the data)
pv <- randomForestSRC::plot.variable(rf, partial = TRUE,
                                      show.plots = FALSE)

## Split into continuous and categorical data frames
result <- gg_partial(pv)
head(result$continuous)
#> # A tibble: 6 × 3
#>       x  yhat name   
#>   <dbl> <dbl> <chr>  
#> 1     7  30.3 Solar.R
#> 2    14  30.4 Solar.R
#> 3    25  30.8 Solar.R
#> 4    44  31.3 Solar.R
#> 5    65  32.7 Solar.R
#> 6    81  36.0 Solar.R

## Label this model for later comparison with a second forest
result_labelled <- gg_partial(pv, model = "airq_model")
unique(result_labelled$continuous$model)
#> [1] "airq_model"