MBGLSS {MBSGS} | R Documentation |
Multivariate Bayesian Group Lasso with Spike and Slab prior
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 |
a |
First shape parameter of the conjugate beta prior for |
b |
Second shape parameter of the conjugate beta prior for |
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 |
d |
Degree of freedom of the inverse Wishart prior of the covariance matrix of the response variable. By default |
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
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)