ypreg: Fitting Yang and Prentice Models

In survstan: Fitting Survival Regression Models via 'Stan'

Fitting Yang and Prentice Models

Description

Function to fit Yang and Prentice (YP) models.

Usage

ypreg(formula, data, baseline = "weibull", dist = NULL, init = 0, ...)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	data an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which function is called.
baseline	the chosen baseline distribution; options currently available are: exponential, weibull, lognormal, loglogistic and Birnbaum-Saunders (fatigue) distributions.
dist	alternative way to specify the baseline distribution (for compatibility with the survreg function); default is NULL.
init	initial values specification (default value is 0); see the detailed documentation for init in optimizing.
...	further arguments passed to other methods.

Value

ypreg returns an object of class "ypreg" containing the fitted model.

Examples

library(survstan)
fit <- ypreg(Surv(futime, fustat) ~ ecog.ps + rx, data = ovarian, baseline = "weibull")
summary(fit)

survstan documentation built on May 29, 2024, 8:41 a.m.