Description Usage Arguments Value Author(s) References Examples
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.
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. 
n_hpp 
Number of the highest posterior probability models which will be returned by the procedure. 
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.
Davide Altomare (davide.altomare@gmail.com).
D. Altomare, G. Consonni and L. La Rocca (2012). Objective Bayesian search of Gaussian directed acyclic graphical models for ordered variables with nonlocal 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 20 21 22 23 24 25 26 27  ## Not run:
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_medGt)))
#Structural Hamming Distance between the true DAG and the highest probability DAG
sum(sum(abs(G_highGt)))
## End(Not run)

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