Description Usage Arguments Value Author(s) References Examples

Estimate the edge inclusion probabilities for a directed acyclic graph (DAG) from observational data, using the moment fractional Bayes factor approach with local prior.

1 |

`Corr` |
qxq correlation matrix. |

`nobs` |
Number of observations. |

`G_base` |
Base DAG. |

`h` |
Parameter prior. |

`C` |
Costant who keeps the probability of all local moves bounded away from 0 and 1. |

`n_tot_mod` |
Maximum number of different models which will be visited by the algorithm, for each equation. |

An object of `class`

`matrix`

with the estimated edge inclusion probabilities.

Davide Altomare (davide.altomare@gmail.com).

D. Altomare, G. Consonni and L. LaRocca (2012).Objective Bayesian search of Gaussian directed acyclic graphical models for ordered variables with non-local priors.*Article submitted to Biometric Methodology*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Not run:
data(SimDag6)
Corr=dataSim6$SimCorr[[1]]
nobs=50
q=ncol(Corr)
Gt=dataSim6$TDag
M_q=FBF_LS(Corr, nobs, matrix(0,q,q), 0, 0.01, 1000)
G_med=M_q
G_med[M_q>=0.5]=1
G_med[M_q<0.5]=0 #median probability DAG
#Structural Hamming Distance between the true DAG and the median probability DAG
sum(sum(abs(G_med-Gt)))
## End(Not run)
``` |

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