FBF_LS {FBFsearch}

Moment Fractional Bayes Factor Stochastic Search with Local Prior for DAG Models

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

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 ([e-mail]).

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

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)))