select {BDgraph} | R Documentation |
Provides the selected graph which, based on input, could be a graph with links for which their estimated posterior probabilities are greater than 0.5 (default) or a graph with the highest posterior probability; see examples.
select( bdgraph.obj, cut = NULL, vis = FALSE )
bdgraph.obj |
A matrix in which each element response to the weight of the links.
It can be an object of |
cut |
Threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; see the examples. |
vis |
Visualize the selected graph structure. |
An adjacency matrix corresponding to the selected graph.
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
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
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 = 7, vis = TRUE ) bdgraph.obj <- bdgraph( data = data.sim ) select( bdgraph.obj ) bdgraph.obj <- bdgraph( data = data.sim, save = TRUE ) select( bdgraph.obj ) select( bdgraph.obj, cut = 0.5, vis = TRUE ) ## End(Not run)