CoefEst {BVSNLP}  R Documentation 
This function estimates coefficient vector for a given set of covariates in a logistic regression and Cox proportional hazard models. It uses the inverse moment nonlocal prior (iMOM) for non zero coefficients.
CoefEst(
X,
resp,
mod_cols,
nlptype = "piMOM",
tau,
r,
family = c("logistic", "survival")
)
X 
The design matrix. It is assumed that the preprocessing steps have
been done on this matrix. It is recommended that to use the output of

resp 
For logistic regression models, this variable is the binary response vector. For Cox proportional hazard models this is a two column matrix where the first column contains the survival time vector and the second column is the censoring status for each observation. 
mod_cols 
A vector of column indices of the design matrix, representing the selected model. 
nlptype 
Determines the type of nonlocal prior that is used in the analyses. It can be "piMOM" for product inverse moment prior, or "pMOM" for product moment prior. The default is set to piMOM prior. 
tau 
Hyperparameter 
r 
Hyperparameter 
family 
Determines the type of data analysis. 
It returns the vector of coefficients for the given model.
Amir Nikooienejad
Nikooienejad, A., Wang, W., and Johnson, V. E. (2016). Bayesian
variable selection for binary outcomes in high dimensional genomic studies
using nonlocal priors. Bioinformatics, 32(9), 13381345.
Nikooienejad, A., Wang, W., & Johnson, V. E. (2020). Bayesian variable
selection for survival data using inverse moment priors. Annals of Applied
Statistics, 14(2), 809828.
### Simulating Survival Data
n < 400
p < 1000
lambda < 0.8
cens_rate < 0.27
set.seed(123)
Sigma < diag(p)
full < matrix(c(rep(0.5, p*p)), ncol=p)
Sigma < full + 0.5*Sigma
cholS < chol(Sigma)
Beta < c(1.8, 1.2, 1.7, 1.4, 1.4, 1.3)
X = matrix(rnorm(n*p), ncol=p)
X = X%*%cholS
X < scale(X)
beta < numeric(p)
beta[c(1:length(Beta))] < Beta
XB < X%*%beta
uvector < log(runif(n));
times < uvector/(lambda*exp(XB))
cens_time < quantile(times,1cens_rate)
status < as.numeric(times < cens_time)
TS < cbind(times,status)
### Estimating coeffcients of the true model and an arbitrary hyper
### parameter for the iMOM prior density
mod < c(1:6)
coef < CoefEst(X, TS, mod, tau = 1.8, r = 2, family = "survival")
coef