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 |
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 |
caic |
Use corrected AIC rather than AIC |
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.
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