Description Usage Arguments Value Author(s) References See Also Examples
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.
1 2 3 4 |
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 |
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 |
Benoit Liquet and Matthew Sutton.
B. Liquet, K. Mengersen, A. Pettitt and M. Sutton. (2016). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Submitted in Bayesian Analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## 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)
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