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Split partial lots into continuous or categorical datasets

Source: R/gg_partial_rfsrc.R
gg_partial_rfsrc.Rd

gg_partial_rfsrc uses the rfsrc::partial.rfsrc to generate the partial plot data internally. So you provide the rfsrc::rfsrc model, and the xvar.names to generate the data.

Usage

gg_partial_rfsrc(
  rf_model,
  xvar.names = NULL,
  xvar2.name = NULL,
  newx = NULL,
  cat_limit = 10
)

Arguments

rf_model

rfsrc::rfsrc model

xvar.names

list(<str>) Which variables to calculate partial plots

xvar2.name

<str> a single grouping feature that is in the newx dataset

newx

a data.frame containing data to use for the partial plots

cat_limit

Categorical features are build when there are fewer than cat_limit unique features.

Examples

## ------------------------------------------------------------
##
## regression
##
## ------------------------------------------------------------

airq.obj <- rfsrc(Ozone ~ ., data = airquality)

## partial effect for wind
prt_dta <- gg_partial_rfsrc(airq.obj,
                       xvar.names = c("Wind"))