plot.bdgraph | R Documentation |
S3
class "bdgraph
" Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability.
## S3 method for class 'bdgraph' plot( x, cut = 0.5, number.g = NULL, main = NULL, layout = igraph::layout_with_fr, vertex.size = 2, vertex.color = "orange", vertex.frame.color = "orange", vertex.label = NULL, vertex.label.dist = 0.5, vertex.label.color = "blue", edge.color = "lightblue", ... )
x |
object of |
cut |
threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; See the examples. |
number.g |
number of graphs with the highest probabilities. This option works for the case running function |
main |
Graphical parameter (see plot). |
layout |
vertex placement which is according to |
vertex.size |
vertex size which is according to |
vertex.color |
vertex color which is according to |
vertex.frame.color |
vertex frame color which is according to |
vertex.label |
vertex label. The default vertex labels are the vertex ids. |
vertex.label.dist |
vertex label distance which is according to |
vertex.label.color |
vertex label color which is according to |
edge.color |
edge color which is according to |
... |
additional plotting parameters. For the complete list, see |
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, doi: 10.18637/jss.v089.i03
bdgraph
, bdgraph.mpl
## Not run: set.seed( 100 ) # Generating multivariate normal data from a 'random' graph data.sim <- bdgraph.sim( n = 100, p = 15, graph = "random", prob = 0.2, vis = TRUE ) bdgraph.obj <- bdgraph( data = data.sim ) plot( bdgraph.obj ) bdgraph.obj <- bdgraph( data = data.sim, save = TRUE ) plot( bdgraph.obj, cut = 0.5 ) plot( bdgraph.obj, number.g = 4 ) ## End(Not run)
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