FBF_GS {FBFsearch} | R Documentation |
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.
FBF_GS(Corr, nobs, G_base, h, C, n_tot_mod, n_hpp)
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 non-local priors. Article submitted to Biometric Methodology.
## 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_med-Gt))) #Structural Hamming Distance between the true DAG and the highest probability DAG sum(sum(abs(G_high-Gt))) ## End(Not run)