FBF_RS {FBFsearch} | R Documentation |
Estimate the edge inclusion probabilities for a regression model (Y(q) on Y(q-1),...,Y(1)) with q variables from observational data, using the moment fractional Bayes factor approach.
FBF_RS(Corr, nobs, G_base, h, C, n_tot_mod, n_hpp)
Corr |
qxq correlation matrix. |
nobs |
Number of observations. |
G_base |
Base model. |
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. 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 Res_search=FBF_RS(Corr, nobs, matrix(0,1,(q-1)), 1, 0.01, 1000, 10) M_q=Res_search$M_q M_G=Res_search$M_G M_P=Res_search$M_P Mt=rev(matrix(Gt[1:(q-1),q],1,(q-1))) #True Model M_med=M_q M_med[M_q>=0.5]=1 M_med[M_q<0.5]=0 #median probability model #Structural Hamming Distance between the true DAG and the median probability DAG sum(sum(abs(M_med-Mt))) ## End(Not run)