survBayes {survBayes} | R Documentation |
Fits a proportional hazards model to time to event data by a Bayesian approach. Right and interval censored data and a lognormal frailty term can be fitted.
survBayes(formula = formula(data), data = parent.frame(), burn.in = 1000, number.sample = 1000, max.grid.size = 50, control, control.frailty, seed.set, ...)
formula |
a formula object, with the response on the left of a ~ operator, and the terms on the right.
The response must be a survival object of type "right" or "interval"
as returned by the Surv function. |
data |
a data.frame in which to interpret the variables named in the formula . |
burn.in |
burn.in |
number.sample |
number of sample |
max.grid.size |
number of grid points |
control |
Object of class control specifying iteration limit and other control options. Default is control(...). |
control.frailty |
Object of class control.frailty specifying iteration limit and other control options.
Default is control.frailty(...). |
seed.set |
setting of the seed of the random number generator |
... |
further parameters |
Fits a proportional hazards model to time to event data by a Bayesian approach. The time axis is split into max.grid.size
intervals and the
log baseline hazard is assumed to be a auto regressive process of order one.
Right and interval censored data and a lognormal frailty term can be fitted. In case of interval censored data the assumed observation times are
augmented by a piecewise exponential distribution conditioned on the respective interval.
The returned values are
t.where |
used grid points |
lbh |
samples of the log baseline hazard at the grid points |
beta |
samples of the vector of covariates |
sigma.lbh |
samples of sigma.lbh.0 and sigma.lbh.1 |
alpha.cluster |
samples of the frailty values |
sigma.cluster |
samples of frailty variance |
m.h.performance |
for beta, lbh and, if appropriate, alpha |
V. Henschel, Ch. Heiss, U. Mansmann
data(AA.data) AA.res<-survBayes(Surv(t.left,t.right,z*3,type="interval")~mo+lok+frailty(gr,dist="gauss"),data=AA.data,burn.in=10,number.sample=10)