FBFsearch (version 1.2)

FBF_GS: Moment Fractional Bayes Factor Stochastic Search with Global Prior for Gaussian DAG Models

Description

Estimate the edge inclusion probabilities for a Gaussian DAG with q nodes from observational data, using the moment fractional Bayes factor approach with global prior.

Usage

FBF_GS(Corr, nobs, G_base, h, C, n_tot_mod, n_hpp)

Value

An object of class

list with:

M_q

Matrix (qxq) with the estimated edge inclusion probabilities.

M_G

Matrix (n*n_hpp)xq with the n_hpp highest posterior probability models returned by the procedure.

M_P

Vector (n_hpp) with the n_hpp posterior probabilities of the models in M_G.

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.

n_hpp

Number of the highest posterior probability models which will be returned by the procedure.

Author

Davide Altomare ().

References

D. Altomare, G. Consonni and L. La Rocca (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

Res_search=FBF_GS(Corr, nobs, matrix(0,q,q), 1, 0.01, 1000, 10)
M_q=Res_search$M_q
M_G=Res_search$M_G
M_P=Res_search$M_P

G_med=M_q
G_med[M_q>=0.5]=1
G_med[M_q<0.5]=0 #median probability DAG

G_high=M_G[1:q,1:q] #Highest Posterior Probability DAG (HPP)
pp_high=M_P[1] #Posterior Probability of the HPP

#Structural Hamming Distance between the true DAG and the median probability DAG
sum(sum(abs(G_med-Gt)))
#Structural Hamming Distance between the true DAG and the highest probability DAG 
sum(sum(abs(G_high-Gt)))