inst/doc/MultiFrailty-intro.R

In MultiFrailty: Shared Frailty Regression Models with Inverse Gaussian, Generalized Lindley, and Gamma Frailty Distributions

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(MultiFrailty)
library(survival)

## ----eval = TRUE--------------------------------------------------------------
library(MultiFrailty)
library(survival)

# Generate synthetic survival data under Gamma frailty with Weibull baseline
set.seed(123)
dat <- r_frailty(n = 80, baseline = "weibull", bpar = c(2.0, 1.5),
                 frailty = "gamma", fpar = c(0.8),
                 x = matrix(rnorm(80), ncol = 1), beta = 0.5)

# Fit model using formula interface
fit <- multifrailty(Surv(time, status) ~ X1, data = dat,
                    baseline = "weibull", frailty = "gamma")

# Summarize fit
summary(fit)

# Predict survival probabilities
pred_surv <- predict_frailty(fit, type = "survival", newtime = c(1, 2, 3))