MBGLSS: Multivariate Bayesian Group Lasso with Spike and Slab prior

In MBSGS: Multivariate Bayesian Sparse Group Selection with Spike and Slab

Description

Run a gibbs sampler for a Multivariate Bayesian group lasso 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

MBGLSS(Y, X, niter = 10000, burnin = 5000, group_size,
a = 1, b = 1, num_update = 100, niter.update = 100,
verbose = FALSE, pi_prior = TRUE, pi = 0.5,
d = 3, update_tau = TRUE, option.update = "global")

Arguments

Y	A numerical vector representing the univariate response variable.
X	A matrix respresenting the design matrix of the linear regression model.
niter	Number of iteration for the Gibbs sampler.
burnin	Number of burnin iteration
group_size	Integer vector representing the size of the groups of the design matrix X
a	First shape parameter of the conjugate beta prior for pi_0. Default is 1.
b	Second shape parameter of the conjugate beta prior for pi_0. Default is 1.
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
verbose	Logical. If "TRUE" iterations are displayed.
pi_prior	Logical. If "TRUE" a beta prior is used for pi
pi	Initial value for pi_0 which will be updated if pi_prior="TRUE""
d	Degree of freedom of the inverse Wishart prior of the covariance matrix of the response variable. By default d is set to 3.
update_tau	Logical. If "TRUE" then a Monte Carlo EM algorithm is used to update lambda
option.update	Two options are proposed for updating lambda. A "Local" update or a "Global" update

Value

BSGSSS returns a list that contains the following components:

pos_mean	The posterior mean estimate of the regression coefficients
pos_median	The posterior mean estimate of the regression coefficients
coef	A matrix with the regression coefficients sampled at each iteration

Author(s)

Benoit Liquet and Matthew Sutton.

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

MBSGSSS

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 <- MBGLSS(Y,X,niter,burnin,gsize,num_update = 100,
niter.update = 100)
model$pos_median[,1]!=0

## End(Not run)

MBSGS documentation built on May 2, 2019, 4:18 a.m.