bayesmc

Xmat

the prior distribution parameter for beta, normal std

the prior distribution parameter for omega

the piror distribution parameter for omega

niter

the sampling probability for updading regresson coefficient

psample

initial survival probabilities at end of study

initsurv

every how many iterations to output parameters

nreport

the survival parameters optimization function

fitsurv

Bayesian method for high-dimensional variable selection

We consider studies in which information from error-prone
diagnostic tests or self-reports are gathered sequentially to determine the
occurrence of a silent event. Using a likelihood-based approach
incorporating the proportional hazards assumption, we provide functions to
estimate the survival distribution and covariate effects. We also provide
functions for power and sample size calculations for this setting.
Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015)
<doi: 10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016)
<doi: 10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes,
Yunsheng Ma, and Raji Balasubramanian (2020) <doi: 10.1186/s12911-020-01223-w>.

bayesmc: Bayesian method for high-dimensional variable selection

Description

Usage

Arguments