predict_frailty: Predictions for MultiFrailty Regression Models

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

Predictions for MultiFrailty Regression Models

Description

Computes survival probabilities, hazard rates, median survival times, expected survival times, risk scores, marginal survival curves, or future survival forecasts for a fitted multifrailty model.

Usage

predict_frailty(
  fit,
  newdata = NULL,
  newtime = NULL,
  type = c("survival", "hazard", "median", "expected", "risk", "marginal", "forecast"),
  window = NULL
)

Arguments

fit	A fitted object of class "multifrailty_fit".
newdata	Optional data frame of new covariate values. If NULL, uses training data.
newtime	Optional vector of evaluation time points. If NULL, uses default grid.
type	Type of prediction: "survival", "hazard", "median", "expected", "risk", "marginal", or "forecast".
window	Optional forecast window or horizon parameter.

Value

Vector or matrix of predictions depending on type.

References

Pandey, A., Hanagal, D. D., & Tyagi, S. (2022). Shared Frailty Models Based on Cancer Data. International Journal of Statistics and Reliability Engineering, 9(3), 461-474.

Pandey, A., & Tyagi, S. (2021). Comparison of Multiplicative Frailty Models Under Weibull Baseline Distribution. Lobachevskii Journal of Mathematics, 42(13), 3184-3195.

Examples

set.seed(123)
dat <- r_frailty(n = 60, baseline = "weibull", bpar = c(2, 1.5), frailty = "gamma", fpar = c(0.8))
fit <- fit_frailty(time = dat$time, status = dat$status, baseline = "weibull", frailty = "gamma")
pred_surv <- predict_frailty(fit, type = "survival", newtime = c(1, 2, 3))

MultiFrailty documentation built on Aug. 8, 2026, 1:07 a.m.