gg_survival: Nonparametric survival estimates.

In ggRandomForests: Visually Exploring Random Forests

Nonparametric survival estimates.

Description

Nonparametric survival estimates.

Usage

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

Arguments

interval	name of the interval variable in the training dataset.
censor	name of the censoring variable in the training dataset.
by	stratifying variable in the training dataset, defaults to NULL
data	name of the training data.frame
type	one of ("kaplan","nelson"), defaults to Kaplan-Meier
...	extra arguments passed to Kaplan or Nelson functions.

Details

gg_survival is a wrapper function for generating nonparametric survival estimates using either nelson-Aalen or kaplan-Meier estimates.

Value

A gg_survival object created using the non-parametric Kaplan-Meier or Nelson-Aalen estimators.

See Also

kaplan nelson

plot.gg_survival

Examples

## Not run: 
## -------- pbc data
data(pbc, package="randomForestSRC")
pbc$time <- pbc$days/364.25

# This is the same as kaplan
gg_dta <- gg_survival(interval="time", censor="status",
                     data=pbc)

plot(gg_dta, error="none")
plot(gg_dta)

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

plot(gg_dta, error="none")
plot(gg_dta)

# ...with smaller confidence limits.
gg_dta <- gg_survival(interval="time", censor="status",
                     data=pbc, by="treatment", conf.int=.68)

plot(gg_dta, error="lines")

## End(Not run)

ggRandomForests documentation built on Sept. 1, 2022, 5:07 p.m.