Description Usage Arguments Author(s) References See Also Examples

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.

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. |

`group_size` |
Integer vector representing the size of the groups of the design matrix |

`pi0` |
Initial value for pi0 which will be updated if |

`pi1` |
Initial value for pi1 which will be updated if |

`a1` |
First shape parameter of the conjugate beta hyper-prior for |

`a2` |
Second shape parameter of the conjugate beta prior for |

`c1` |
First shape parameter of the conjugate beta hyper-prior for |

`c2` |
Second shape parameter of the conjugate beta prior for |

`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 |

`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 |

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$gsize
niter <- 2000
burnin <- 1000
model <- MBSGSSS(Y,X,niter=niter,burnin=burnin,group_size=gsize,
num_update = 50,niter.update = 50)
model$pos_median[,1]!=0
## End(Not run)
``` |

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