MBSGS (version 1.1.0)

MBSGSSS: Multivariate Bayesian Sparse Group Selection with Spike and Slab priors

Description

Run a gibbs sampler for a Multivariate Bayesian sparse group selection model with spike and slab prior. This function is designed for a regression model with multivariate response, where the design matrix has a group structure.

Usage

MBSGSSS(Y, X, group_size, pi0 = 0.5, pi1 = 0.5,
a1 = 1, a2 = 1, c1 = 1,c2 = 1, pi_prior = TRUE,
niter = 10000, burnin = 5000, d = 3,
num_update = 100, niter.update = 100)

Arguments

Y

A numerical vector representing the univariate response variable.

X

A matrix respresenting the design matrix of the linear regression model.

group_size

Integer vector representing the size of the groups of the design matrix X

pi0

Initial value for pi0 which will be updated if pi_prior="TRUE"

pi1

Initial value for pi1 which will be updated if pi_prior="TRUE"

a1

First shape parameter of the conjugate beta hyper-prior for pi_0. Default is 1.

a2

Second shape parameter of the conjugate beta prior for pi_0. Default is 1.

c1

First shape parameter of the conjugate beta hyper-prior for pi_1. Default is 1.

c2

Second shape parameter of the conjugate beta prior for pi_1. Default is 1.

pi_prior

Logical. If "TRUE" beta priors are used for pi0 and pi1

niter

Number of iteration for the Gibbs sampler.

burnin

Number of burnin iteration

d

Degree of freedom of the inverse Wishart prior of the covariance matrix of the response variable. By default d is set to 3.

num_update

Number of update regarding the scaling of the shrinkage parameter lambda which is calibrated by a Monte Carlo EM algorithm

niter.update

Number of itertion regarding the scaling of the shrinkage parameter lambda which is calibrated by a Monte Carlo EM algorithm

References

B. Liquet, K. Mengersen, A. Pettitt and M. Sutton. (2016). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Submitted in Bayesian Analysis.

See Also

MBGLSS

Examples

# NOT RUN {
## Simulation of datasets X and Y with group variables
data1 = gen_data_Multi(nsample = 120, ntrain = 80)
data1 = Mnormalize(data1)

true_model <- data1$true_model
X <- data1$X
Y<- data1$Y
train_idx <- data1$train_idx
gsize <- data1$gsize
niter <- 2000
burnin <- 1000

model <- MBSGSSS(Y,X,niter=niter,burnin=burnin,group_size=gsize,
num_update = 50,niter.update = 50)
model$pos_median[,1]!=0
# }