The list containing observed data from n subjects;
t, di, x
priorPara
The list containing prior parameter values;
eta0, kappa0, c0, r, delta,
s, groupInd
initial
The list containing the starting values of the parameters;
beta.ini,
lambdaSq, sigmaSq, tauSq, h
rw
When setting to "TRUE", the conventional random walk Metropolis Hastings algorithm is used.
Otherwise, the mean and the variance of the proposal density is updated using the jumping rule described in Lee et al. (2011).
mcmcPara
The list containing the values of options for Metropolis-Hastings step for β;
numBeta, beta.prop.var
num.reps
the number of iterations of the chain
thin
thinning
chain
the numeric name of chain in the case when running multiple chains.
save
frequency of storing the results in .Rdata file.
For example, by setting "save = 1000", the algorithm saves the results every 1000 iterations.
Details
t
a vector of n times to the event
di
a vector of n censoring indicators for the event time (1=event occurred, 0=censored)
x
covariate matrix, n observations by p variables
eta0
scale parameter of gamma process prior for the cumulative baseline hazard, eta0 > 0
kappa0
shape parameter of gamma process prior for the cumulative baseline hazard, kappa0 > 0
c0
the confidence parameter of gamma process prior for the cumulative baseline hazard, c0 > 0
r
the shape parameter of the gamma prior for λ^2
delta
the rate parameter of the gamma prior for λ^2
s
the set of time partitions for specification of the cumulative baseline hazard function
groupInd
a vector of p group indicator for each variable
beta.ini
the starting values for β
lambdaSq
the starting value for λ^2
sigmaSq
the starting value for σ^2
tauSq
the starting values for τ^2
h
the starting values for h
numBeta
the number of components in β to be updated at one iteration
beta.prop.var
the variance of the proposal density for β when rw is set to "TRUE"
Value
psbcGL returns an object of class psbcGL
beta.p
posterior samples for β
h.p
posterior samples for h
tauSq.p
posterior samples for τ^2
mcmcOutcome
The list containing posterior samples for the remaining model parameters
Note
To fit the PSBC model with the ordinary Bayesian lasso prior (Lee et al., 2011), groupInd needs to be set to 1:p.
If the prespecified value of save is less than that of num.reps, the results are saved
as .Rdata file under the directory working directory/mcmcOutcome.
Author(s)
Kyu Ha Lee, Sounak Chakraborty, (Tony) Jianguo Sun
References
Lee, K. H., Chakraborty, S., and Sun, J. (2011).
Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data.
The International Journal of Biostatistics, Volume 7, Issue 1, Pages 1-32.
Lee, K. H., Chakraborty, S., and Sun, J. (2015).
Survival Prediction and Variable Selection with Simultaneous Shrinkage and Grouping Priors. Statistical Analysis and Data Mining, Volume 8, Issue 2, pages 114-127.