Description Usage Arguments Value Author(s) References See Also Examples
This function calculates the logarithm of unnormalized probability of a given set of covariates for both survival and binary response data. It uses the inverse moment nonlocal prior (iMOM) for non zero coefficients and beta binomial prior for the model space.
1 2 
X 
The design matrix. It is assumed that the design matrix has
standardized columns. 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 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 
a 
First parameter in the beta binomial prior. 
b 
Second parameter in the beta binomial prior. 
family 
Determines the type of data analysis. 
It returns the unnormalized probability for the selected 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., and Johnson, V. E. (2017). Bayesian Variable
Selection in High Dimensional Survival Time Cancer Genomic Datasets using
Nonlocal Priors. arXiv preprint, arXiv:1712.02964.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  ### Simulating Logistic Regression Data
n < 400
p < 1000
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(1,1.8,2.5)
X = matrix(rnorm(n*p), ncol=p)
X = X%*%cholS
beta < numeric(p)
beta[c(1:length(Beta))] < Beta
XB < X%*%beta
probs < as.vector(exp(XB)/(1+exp(XB)))
y < rbinom(n,1,probs)
### Calling the function for a subset of the true model, with an arbitrary
### parameters for prior densities
mod < c(1:3)
Mprob < ModProb(X, y, mod, tau = 0.7, r = 1, a = 7, b = 993,
family = "logistic")
Mprob

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