Partial dependence data from an rfsrc model

A partial dependence curve marginalizes the forest's prediction over all other predictors: for each evaluation point of the target variable, the forest scores every training observation with that value substituted in, then averages the result. What you get is the average effect of the target variable after "integrating out" the rest – a curve that would be flat if the variable carried no signal.

Usage

gg_partial_rfsrc(
  rf_model,
  xvar.names = NULL,
  xvar2.name = NULL,
  newx = NULL,
  partial.time = NULL,
  partial.type = c("surv", "chf", "mort"),
  cat_limit = 10,
  n_eval = 25
)

Arguments

rf_model: A fitted rfsrc object.
xvar.names: Character vector of predictor names for which partial dependence should be computed. Must be a subset of rf_model$xvar.names.
xvar2.name: Optional single character name of a grouping variable in newx. When supplied, partial dependence is computed separately for each unique level of this variable and a grp column is appended.
newx: Optional data.frame of predictor values to evaluate partial effects at. Defaults to the training data stored in rf_model$xvar. All column names must match rf_model$xvar.names.
partial.time: Numeric vector of desired time points for survival forests (ignored for regression/classification). Values are automatically snapped to the nearest entry in rf_model$time.interest; see the Survival forests section below. When NULL (default), three quartile points of time.interest are used.
partial.type: Character; type of predicted value for survival forests, passed through to partial.rfsrc. One of "surv" (default), "chf", or "mort". Ignored for non-survival forests. partial.rfsrc() requires a non-NULL value for survival families; supplying it here avoids a cryptic “argument is of length zero” error from the underlying C code.
cat_limit: Variables with fewer than cat_limit unique values in newx are treated as categorical; all others are continuous. Defaults to 10.
n_eval: Number of evaluation points for continuous variables. Instead of passing all observed values (which can be slow, especially for survival forests), continuous predictors are evaluated on a quantile grid of this many points. Categorical variables always use all unique levels. Defaults to 25.

Value

A named list with two elements:

continuous: A data.frame with columns x (numeric), yhat, name (variable name), and optionally grp (the level of xvar2.name) and time (survival forests only) for all continuous predictors.
categorical: A data.frame with the same columns but x kept as character, for low-cardinality predictors.

Details

This function builds those curves for one or more predictors by calling partial.rfsrc and then tidy-stacking the results into separate data frames for continuous and categorical variables. Unlike gg_partial (which wraps plot.variable), you pass the fitted rfsrc object directly – no intermediate plot.variable step.

For survival forests, the marginalized quantity depends on partial.type: survival probability ("surv"), cumulative hazard function ("chf"), or expected mortality ("mort"). You can request the curve at one or more time horizons via partial.time; the resulting data have a time column so the plot layers them as separate coloured lines.

Survival forests and `partial.time`

partial.rfsrc expects every value in partial.time to be an exact member of the model's time.interest vector, the unique observed event times stored in the fitted object. Pass an arbitrary time, even a plausible one such as c(1, 3) for a study measured in years, and you get a C-level prediction error from inside partial.rfsrc.

gg_partial_rfsrc takes care of this: every element of partial.time is silently snapped to its nearest time.interest value before the call. To target a specific follow-up horizon, find the closest grid point yourself and pass it explicitly:


ti  <- rf_model$time.interest
t1  <- ti[which.min(abs(ti - 1))]   # nearest to 1 year
pd  <- gg_partial_rfsrc(rf_model, xvar.names = "x", partial.time = t1)

Logical predictor columns

partial.rfsrc does not handle logical predictor columns correctly in survival forests (randomForestSRC <= 3.5.1). If your training data contains binary 0/1 columns, convert them to factor rather than logical before fitting the model.

Examples

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

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

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