# FBF_LS: Moment Fractional Bayes Factor Stochastic Search with Local... In FBFsearch: Algorithm for Searching the Space of Gaussian Directed Acyclic Graph Models Through Moment Fractional Bayes Factors

## Description

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

## Usage

 `1` ```FBF_LS(Corr, nobs, G_base, h, C, n_tot_mod) ```

## Arguments

 `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.

## Value

An object of `class` `matrix` with the estimated edge inclusion probabilities.

## Author(s)

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

## References

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.

## Examples

 ``` 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[] 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) ```

FBFsearch documentation built on May 2, 2019, 2:26 p.m.