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Nonparametric survival estimates.

Source: R/gg_survival.R
gg_survival.Rd

Nonparametric survival estimates.

Usage

gg_survival(
  object = NULL,
  interval = NULL,
  censor = NULL,
  by = NULL,
  data = NULL,
  type = c("kaplan", "nelson"),
  ...
)

# S3 method for class 'rfsrc'
gg_survival(
  object,
  interval = NULL,
  censor = NULL,
  by = NULL,
  data = NULL,
  type = c("kaplan", "nelson"),
  ...
)

# Default S3 method
gg_survival(
  object = NULL,
  interval = NULL,
  censor = NULL,
  by = NULL,
  data = NULL,
  type = c("kaplan", "nelson"),
  ...
)

Arguments

object

For the rfsrc method: a fitted rfsrc survival forest. For the default method: pass NULL (or omit) and supply interval, censor, and data instead.

interval

Character; name of the time-to-event column in data (default method only).

censor

Character; name of the event-indicator column in data (1 = event, 0 = censored; default method only).

by

Optional character; name of a grouping column for stratified estimates. For the rfsrc method, by must be a column in object$xvar.

data

A data.frame containing survival data (default method only).

type

One of "kaplan" (Kaplan-Meier, default) or "nelson" (Nelson-Aalen cumulative hazard). Default method only.

...

Additional arguments passed to kaplan or nelson.

Value

A gg_survival data.frame with columns time, surv, cum_haz, lower, upper, n.risk, and optionally groups when by is supplied.

Details

Comparing the forest's ensemble survival curve to the marginal Kaplan-Meier baseline is a quick sanity check: if they diverge the forest has found structure the predictors carry; if they track each other closely the predictors may add little. gg_survival computes the nonparametric baseline – the Kaplan-Meier or Nelson-Aalen estimate – so you can place it on the same canvas as the forest predictions from gg_rfsrc.

gg_survival is an S3 generic that dispatches on the class of its first argument:

rfsrc

Extracts the outcome columns from the fitted forest's $yvar slot (time in column 1, event indicator in column 2) and delegates to kaplan. Use by to stratify on a predictor from $xvar: you get one Kaplan-Meier curve per group, ready to compare against the forest's group-specific ensemble curves.

default

Accepts raw survival columns directly via interval, censor, and data. Delegates to kaplan (the default) or nelson depending on type.

Note

Survival estimation is randomForestSRC-only; randomForest has no survival forest, so no randomForest method exists.

See also

kaplan nelson

plot.gg_survival

Examples

## -------- pbc data (default method, raw data columns)
data(pbc, package = "randomForestSRC")
pbc$time <- pbc$days / 364.25

gg_dta <- gg_survival(interval = "time", censor = "status", data = pbc)
plot(gg_dta, error = "none")


# Stratified
gg_dta <- gg_survival(
  interval = "time", censor = "status",
  data = pbc, by = "treatment"
)
plot(gg_dta)