BSBHaz {BGPhazard}  R Documentation 
BSBHaz posterior samples using Gibbs Sampler
Description
BSBHaz
samples posterior observations from the bivariate survival
model (BSBHaz model) proposed by NietoBarajas & Walker (2007).
Usage
BSBHaz(
bsb_init,
iter,
burn_in = 0,
omega_d = NULL,
gamma_d = NULL,
theta_d = NULL,
seed = 42
)
Arguments
bsb_init 
An object of class 'BSBinit' created by

iter 
A positive integer. Number of samples generated by the Gibbs Sampler. 
burn_in 
A positive integer. Number of iterations that should be discarded as burn in period. 
omega_d 
A positive double. This parameter defines the interval used in the MetropolisHastings algorithm to sample proposals for omega. See details. 
gamma_d 
A positive double. This parameter defines the interval used in the MetropolisHastings algorithm to sample proposals for gamma. See details. 
theta_d 
A positive double. This parameter defines the interval used in the MetropolisHastings algorithm to sample proposals for theta. See details. 
seed 
Random seed used in sampling. 
Details
BSBHaz (NietoBarajas & Walker, 2007) is a bayesian semiparametric model for bivariate survival data. The marginal densities are nonparametric survival models and the joint density is constructed via a mixture. Dependence between failure times is modeled using two frailties, and the dependence between these frailties is modeled with a copula.
This command obtains posterior samples from model parameters. The samples
from omega, gamma, and theta are obtained using the MetropolisHastings
algorithm. The proposal distributions are uniform for the three parameters.
The parameters omega_d
, gamma_d
and theta_d
modify the
intervals from which the uniform proposals are sampled. If these parameters
are too large, the acceptance rates will decrease and the chains will get
stuck. On the other hand, if these parameters are small, the acceptance rates
will be too high and the chains will not explore the posterior support
effectively.
Value
An object of class 'BSBHaz
' containing the samples from the
variables of interest.
Examples
t1 < survival::Surv(c(1, 2, 3))
t2 < survival::Surv(c(1, 2, 3))
init < BSBInit(t1 = t1, t2 = t2, seed = 0)
samples < BSBHaz(init, iter = 10, omega_d = 2,
gamma_d = 10, seed = 10)