utilities_internal: Advanced Utility Functions for RHF Workflows

In randomForestRHF: Random Hazard Forests

Advanced Utility Functions for RHF Workflows

Description

These helper functions support advanced random-hazard-forest workflows, including data conversion, simulation, and predictor weighting.

Usage

convert.counting(f, dta, scale = FALSE)

convert.standard.counting(formula, data,
  scale = FALSE,
  rescale.from.attr = FALSE,
  keep.id = FALSE,
  keep.row_index = FALSE,
  sorted = FALSE,
  id.default = "id",
  eps = 1e-8,
  landmark.time = NULL,
  landmark.use.tminus = TRUE,
  return.type = c("survival", "x"),
  keep.landmark.cols = FALSE)

hazard.simulation(type = 1,
  n = 500, p = 10, nrecords = 7,
  scale = FALSE, ngrid = 1e5, ...)

xvar.wt.rhf(f, d, scale = 4, parallel = TRUE)

Arguments

f, formula	A survival formula. convert.counting() expects a standard survival formula such as Surv(time, event) ~ .. convert.standard.counting() expects counting-process style input such as Surv(id, start, stop, event) ~ . or Surv(start, stop, event) ~ ..
dta, data, d	Input data frame.
scale	Logical or numeric time-scaling control, depending on the helper being used.
rescale.from.attr	Logical; if TRUE, convert.standard.counting() rescales times using attributes stored on the counting-process data.
keep.id	Logical; if TRUE, retain the subject identifier in the returned data frame from convert.standard.counting().
keep.row_index	Logical; if TRUE, include the source row index in the output of convert.standard.counting().
sorted	Logical; indicates whether the counting-process input is already sorted by subject and time.
id.default	Default subject identifier column name used when formula omits an explicit id term.
eps	Small positive tolerance used in internal time comparisons.
landmark.time	Optional landmark time passed to convert.standard.counting().
landmark.use.tminus	Logical; controls how landmark covariates are selected when landmark.time is supplied.
return.type	Character string specifying whether convert.standard.counting() returns a standard survival outcome plus covariates ("survival") or only the covariate frame ("x").
keep.landmark.cols	Logical; if TRUE, keep auxiliary landmark columns in the output of convert.standard.counting().
type	Simulation scenario. May be given numerically (1, 2, 3) or by name.
n, p, nrecords, ngrid	Simulation controls used by hazard.simulation().
...	Additional arguments passed to the selected simulation engine.
parallel	Logical; if TRUE, allow parallel work in xvar.wt.rhf().

Details

convert.counting() converts standard right-censored survival data to the counting-process representation expected by rhf().

convert.standard.counting() converts counting-process style RHF data to a conventional survival-analysis data frame or to a predictor-only frame.

hazard.simulation() generates synthetic counting-process data together with the corresponding theoretical hazard function.

xvar.wt.rhf() computes variable weights that can be supplied to rhf(..., xvar.wt = ...).

Value

The returned value depends on the helper:

• convert.counting(): a data frame with columns id, start, stop, event, followed by the predictor columns from the input data.

• convert.standard.counting(): a data frame in the format requested by return.type. With return.type = "survival", the leading columns are time and event; additional covariate and optional helper columns may also be present. The function may return an empty data frame when no valid rows remain after filtering.

• hazard.simulation(): a list with components dta (simulated counting-process data frame), haz (a function that evaluates the true hazard on a supplied time grid), and scale (the time-scaling factor used in the simulation).

• xvar.wt.rhf(): a named numeric vector of predictor weights for the xvar.wt argument of rhf().

See Also

rhf, predict.rhf

randomForestRHF documentation built on April 24, 2026, 1:07 a.m.