FitGamma: Gamma Distribution Parameter Estimation

In Temporal: Parametric Time to Event Analysis

Gamma Distribution Parameter Estimation

Description

Estimates parameters for gamma event times subject to non-informative right censoring. The gamma distribution is parameterized in terms of the shape \alpha and rate \lambda:

f(t) = \frac{\lambda}{\Gamma(\alpha)}(\lambda t)^{\alpha-1}e^{-\lambda t}, t>0

Usage

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

Arguments

data	Data.frame.
eps	Tolerance for Newton-Raphson iterations.
init	List with initial values for the 'shape' \alpha and 'rate' \lambda.
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.

Value

An object of class fit containing the following:

Parameters

The estimated shape \alpha and rate \lambda.

Information

The observed information matrix.

Outcome

The fitted mean, median, and variance.

RMST

The estimated RMSTs, if tau was specified.

Examples

# Generate Gamma data with 20% censoring.
data <- GenData(n = 1e3, dist = "gamma", theta = c(2, 2), p = 0.2)

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

Temporal documentation built on Sept. 24, 2023, 1:06 a.m.