frailty.fit {extrafrail} | R Documentation |
Fitted different shared frailty models
Description
frailty.fit computes the maximum likelihood estimates based on the EM algorithm for the shared gamma, inverse gaussian, weighted Lindley, Birnbaum-Saunders, truncated normal, mixture of inverse gaussian and mixture of Birbaum-Saunders frailty models.
Usage
frailty.fit(formula, data, dist.frail="gamma", dist = "np", prec = 1e-04,
max.iter = 1000, part=NULL)
Arguments
formula |
A formula that contains on the left hand side an object of the type Surv and on the right hand side a +cluster(id) statement, possibly with the covariates definition. |
data |
A data.frame in which the formula argument can be evaluated |
dist.frail |
the distribution assumed for the frailty. Supported values: gamma (GA also is valid), IG (inverse gaussian), WL (weighted Lindley), BS (Birnbaum-Saunders), TN (truncated normal), MIG (mixture of IG), MBS (mixture of BS) and GE (generalized exponential). |
dist |
the distribution assumed for the basal model. Supported values: weibull, pe (piecewise exponential), exponential and np (non-parametric). |
prec |
The convergence tolerance for parameters. |
max.iter |
The maximum number of iterations. |
part |
partition time (only for piecewise exponential distribution). |
Details
For the weibull, exponential and piecewise exponential distributions as the basal model, the M1-step is performed using the optim function. For the non-parametric case, the M1-step is based on the coxph function from the survival package.
Value
an object of class "extrafrail" is returned. The object returned for this functions is a list containing the following components:
coefficients |
A named vector of coefficients |
se |
A named vector of the standard errors for the estimated coefficients. |
t |
The vector of times. |
delta |
The failure indicators. |
id |
A variable indicating the cluster which belongs each observation. |
x |
The regressor matrix based on cov.formula (without intercept term). |
dist |
The distribution assumed for the basal model. |
dist.frail |
The distribution assumed for the frailty variable. |
tau |
The Kendall's tau coefficient. |
logLik |
The log-likelihood function (only when the Weibull model is specified for the basal distribution). |
Lambda0 |
The observed times and the associated cumulative hazard function (only when the non-parametric option is specified for the basal distribution) |
part |
the partition time (only for piecewise exponential model). |
Author(s)
Diego Gallardo, Marcelo Bourguignon and John Santibanez.
References
Gallardo, D.I., Bourguignon, M. (2022) The shared weighted Lindley frailty model for cluster failure time data. Biometrical journal, 67, e70044.
Gallardo, D.I., Bourguignon, M., Romeo, J. (2024) Birnbaum-Saunders frailty regression models for clustered survival data. Statistics and Computing, 34, 141.
Examples
require(survival)
#require(frailtyHL)
data(rats, package="frailtyHL")
#Fit for WL frailty model
fit.WL <- frailty.fit(survival::Surv(time, status)~ rx+ survival::cluster(litter),
dist.frail="WL", data = rats)
summary(fit.WL)
#Fit for gamma frailty model
fit.GA <- frailty.fit(survival::Surv(time, status) ~ rx + survival::cluster(litter),
dist.frail="gamma", data = rats)
summary(fit.GA)