plot.graph {BDgraph} | R Documentation |
S3
class "graph"
Visualizes structure of the graph.
## S3 method for class 'graph' plot( x, cut = 0.5, mode = "undirected", diag = FALSE, main = NULL, vertex.color = "white", vertex.label.color = 'black', ... )
x |
An object of |
cut |
This option is for the case where input 'x' is the object of class "bdgraph" or "ssgraph". Threshold for including the links in the selected graph based on the estimated posterior probabilities of the links. |
mode |
Type of graph which is according to |
diag |
Logical which is according to |
main |
Graphical parameter (see plot). |
vertex.color |
The vertex color which is according to |
vertex.label.color |
The vertex label color which is according to |
... |
System reserved (no specific usage). |
Reza Mohammadi a.mohammadi@uva.nl
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
Mohammadi, A. and Dobra, A. (2017). The R
Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16
# Generating a 'random' graph adj <- graph.sim( p = 10, graph = "random" ) plot( adj ) adj