Advanced Utility Functions for RHF Workflows

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

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().

Arguments

Details

Value

See also