cox_bvs {BVSNLP}  R Documentation 
This function performs Bayesian variable selection for
survival data in a nonparallel fashion. It runs modified S5 algorithm to
search the model space but since this is only on one CPU, the number of
visited models will not be large and therefore is NOT recommended for
high dimensional datasets. This function is called by bvs
function in a parllel fashion and therefore that function is recommended
to be used.
cox_bvs(exmat, cur_cols, nf, tau, r, nlptype, a, b, d, L, J, temps)
exmat 
An extended matrix where the first two columns are survival
times and status, respectively and the rest is the design matrix which is
produced by 
cur_cols 
A vector containing indices of the initial model for
variable selector to start the S5 algorithm from. Note that the first

nf 
The number of fixed covariates that do not enter the selection procedure. 
tau 
The paramter 
r 
The paramter 
nlptype 
Determines the type of nonlocal prior that is used in the
analyses. 
a 
The first parameter in beta distribution used as prior on model size. This parameter is equal to 1 when uinformbinomial prior is used. 
b 
The second paramter in beta distribution used as prior on model size. This parameter is equal to 1 when uinformbinomial prior is used. 
d 
This is the number of candidate covariates picked from top variables with highest utility function value and used in S5 algorithm. 
L 
Number of temperatures in S5 algorithm. 
J 
Number of iterations at each temperature in S5 algorithm. 
temps 
Vector of temperatuers used in S5 algorithm. 
It returns a list containing following objects:
max_model 
A 
hash_key 
A column vector indicating the generated key for each model that is used to track visited models and growing dictionary. 
max_prob 
The unnormalized probability of the model with highest posterior probability. 
all_probs 
A vector containing unnormalized probabilities of all visited models. 
vis_covs_list 
A list containing the covariates in each visited model in the stochastic search process. 
Amir Nikooienejad
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.
Shin, M., Bhattacharya, A., and Johnson, V. E. (2017). Scalable
Bayesian variable selection using nonlocal prior densities in ultrahigh
dimensional settings. Statistica Sinica.
Johnson, V. E., and Rossell, D. (2010). On the use of nonlocal prior
densities in Bayesian hypothesis tests. Journal of the Royal Statistical
Society: Series B (Statistical Methodology), 72(2), 143170.
### Initializing the parameters
n < 100
p < 40
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
sur_times < rexp(n,exp(XB))
cens_times < rexp(n,0.2)
times < pmin(sur_times,cens_times)
status < as.numeric(sur_times <= cens_times)
exmat < cbind(times,status,X)
L < 10; J < 10
d < 2 * ceiling(log(p))
temps < seq(3, 1, length.out = L)
tau < 0.5; r < 1; a < 6; b < pa
nlptype < 0 ### PiMOM nonlocal prior
cur_cols < c(1,2,3) ### Starting model for the search algorithm
nf < 0 ### No fixed columns
### Running the Function
coxout < cox_bvs(exmat,cur_cols,nf,tau,r,nlptype,a,b,d,L,J,temps)
### The number of visited model for this specific run:
length(coxout$hash_key)
### The selected model:
which(coxout$max_model>0)
### The unnormalized probability of the selected model:
coxout$max_prob