# 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[[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)

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