plotcoda {BDgraph} | R Documentation |
Visualizes the cumulative occupancy fractions of all possible links in the graph. It can be used for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.
plotcoda( bdgraph.obj, thin = NULL, control = TRUE, main = NULL, ... )
bdgraph.obj |
An object of |
thin |
An option for getting fast result for a cumulative plot according to part of the iteration. |
control |
Logical: if TRUE (default) and the number of nodes is greater than 15, then 100 links randomly is selected for visualization. |
main |
Graphical parameter (see plot). |
... |
System reserved (no specific usage). |
Note that a spending time for this function depends on the number of nodes.
For fast result, you can choose bigger value for the 'thin'
option.
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
bdgraph
, bdgraph.mpl
, traceplot
## Not run: # Generating multivariate normal data from a 'circle' graph data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE ) bdgraph.obj <- bdgraph( data = data.sim, iter = 10000, burnin = 0 , save = TRUE ) plotcoda( bdgraph.obj ) ## End(Not run)