pgraph {BDgraph} | R Documentation |
Provides the estimated posterior probabilities for the most likely graphs or a specific graph.
pgraph( bdgraph.obj, number.g = 4, adj = NULL )
bdgraph.obj |
An object of |
number.g |
The number of graphs with the highest posterior probabilities to be shown.
This option is ignored if |
adj |
An adjacency matrix corresponding to a graph structure. It is an upper triangular matrix in which
a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0.
It also can be an object of |
selected_g |
the adjacency matrices which corresponding to the graphs with the highest posterior probabilities. |
prob_g |
A vector of the posterior probabilities of the graphs corresponding to |
Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit
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
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645
## 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)