pgraph {BDgraph} | R Documentation |
Posterior probabilities of the graphs
Description
Provides the estimated posterior probabilities for the most likely graphs or a specific graph.
Usage
pgraph( bdgraph.obj, number.g = 4, adj = NULL )
Arguments
bdgraph.obj |
object of |
number.g |
number of graphs with the highest posterior probabilities to be shown.
This option is ignored if |
adj |
adjacency matrix corresponding to a graph structure. It is an upper triangular matrix in which
|
Value
selected_g |
adjacency matrices which corresponding to the graphs with the highest posterior probabilities. |
prob_g |
vector of the posterior probabilities of the graphs corresponding to |
Author(s)
Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit
References
Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R
Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30, doi:10.18637/jss.v089.i03
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, doi:10.1214/14-BA889
Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, doi:10.1080/01621459.2021.1996377
See Also
Examples
## Not run:
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 6, vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim, save = TRUE )
# Estimated posterior probability of the true graph
pgraph( bdgraph.obj, adj = data.sim )
# Estimated posterior probability of first and second graphs with highest probabilities
pgraph( bdgraph.obj, number.g = 2 )
## End(Not run)