Temporal (version 0.3.0.1)

FitGenGamma: Generalized Gamma Distribution Parameter Estimation

Description

Estimates parameters for generalized gamma event times subject to non-informative right censoring. The gamma distribution is parameterized in terms of the shape parameters \((\alpha,\beta)\), and the rate \(\lambda\): $$f(t) = \frac{\beta\lambda}{\Gamma(\alpha)} (\lambda t)^{\alpha\beta-1}e^{-(\lambda t)^{\beta}}, t>0$$

Usage

FitGenGamma(
  data,
  beta_lower = 0.1,
  beta_upper = 10,
  eps = 1e-06,
  init = list(),
  maxit = 10,
  report = FALSE,
  sig = 0.05,
  status_name = "status",
  tau = NULL,
  time_name = "time"
)

Value

An object of class fit containing the following:

Parameters

The estimated shape \((\alpha,\beta)\) and rate \(\lambda\) parameters.

Information

The observed information matrix.

Outcome

The fitted mean, median, and variance.

RMST

The estimated RMSTs, if tau was specified.

Arguments

data

Data.frame.

beta_lower

If dist="gen-gamma", lower limit on possible values for beta.

beta_upper

If dist="gen-gamma", upper limit on possible values for beta.

eps

Tolerance for Newton-Raphson iterations.

init

List with initial values for the shape `alpha`, `beta` and rate `lambda` parameters.

maxit

Maximum number of NR iterations.

report

Report fitting progress?

sig

Significance level, for CIs.

status_name

Name of the status indicator, 1 if observed, 0 if censored.

tau

Optional truncation times for calculating RMSTs.

time_name

Name of column containing the time to event.

Examples

set.seed(103)
# Generate generalized gamma data with 20% censoring.
data <- GenData(n = 1e4, dist = "gen-gamma", theta = c(2, 2, 2), p = 0.2)

# Estimate parameters.
fit <- FitParaSurv(data, dist = "gen-gamma", report = TRUE)