residuals_frailty: Residual Diagnostics and Goodness-of-Fit Tests for...

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

Residual Diagnostics and Goodness-of-Fit Tests for MultiFrailty Models

Description

Computes Cox-Snell, Martingale, Deviance, raw, standardized, and studentized residuals along with Kolmogorov-Smirnov (K-S) goodness-of-fit statistics against standard Exponential(1).

Usage

residuals_frailty(fit)

Arguments

fit	A fitted object of class "multifrailty_fit".

Value

A list containing residual vectors, summary accuracy metrics (MSE, RMSE, MAE, R_square, Adj_R_square), and K-S test results (KS_stat, KS_pvalue).

References

Cox, D. R., & Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society: Series B (Methodological), 30(2), 248-265.

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")
res <- residuals_frailty(fit)
print(res$KS_pvalue)

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