FBF_LS {FBFsearch} | R Documentation |
Estimate the edge inclusion probabilities for a directed acyclic graph (DAG) from observational data, using the moment fractional Bayes factor approach with local prior.
FBF_LS(Corr, nobs, G_base, h, C, n_tot_mod)
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. |
An object of class
matrix
with the estimated edge inclusion probabilities.
Davide Altomare (davide.altomare@gmail.com).
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.
## 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)