| frailty {survival} | R Documentation |
When included in a coxph or survreg, fits
by penalised likelihood a random effects (frailty) model. frailty is generic, with methods for t, Gaussian and Gamma distributions.
frailty(x, distribution="gamma", ...)
frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05, method = c("em","aic", "df", "fixed"), ...)
frailty.gaussian(x, sparse = (nclass > 5), theta, df, method = c("reml","aic", "df", "fixed"), ...)
frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5,method = c("aic", "df", "fixed"), ...)
x |
group indicator |
distribution |
frailty distribution |
... |
Arguments for specific distribution, including (but not limited to) |
sparse |
Use sparse Newton-Raphson algorithm |
df |
Approximate degrees of freedom |
theta |
Penalty |
eps |
Accuracy of df |
method |
maximisation algorithm |
tdf |
df of t-distribution |
The penalised likelihood method is equivalent to maximum (partial) likelihood for the gamma frailty but not for the others.
The sparse algorithm uses the diagonal of the information matrix for the random effects, which saves a lot of space.
The frailty distributions are really the log-t and lognormal: t and Gaussian are random effects on the scale of the linear predictor.
An object of class coxph.penalty containing a factor with attributes specifying the control functions.
Therneau TM, Grambsch PM, Pankratz VS (2003) "Penalized survival models and frailty" Journal of Computational and Graphical Statistics 12, 1: 156-175
data(kidney)
kfit <- coxph(Surv(time, status)~ age + sex + disease + frailty(id), kidney)
kfit0 <- coxph(Surv(time, status)~ age + sex + disease, kidney)
kfitm1 <- coxph(Surv(time,status) ~ age + sex + disease +
frailty(id, dist='gauss'), kidney)
coxph(Surv(time, status) ~ age + sex + frailty(id, dist='gauss', method='aic',caic=TRUE), kidney)
# uncorrected aic
coxph(Surv(time, status) ~ age + sex + frailty(id, method='aic', caic=FALSE), kidney)
data(rats)
rfit2a <- survreg(Surv(time, status) ~ rx +
frailty.gaussian(litter, df=13, sparse=FALSE), rats )
rfit2b <- survreg(Surv(time, status) ~ rx +
frailty.gaussian(litter, df=13, sparse=TRUE), rats )
rfit2a
rfit2b