print.sim {BDgraph} | R Documentation |
S3
class "sim"
Prints the information about the type of data, the sample size, the graph type, the number of nodes, number of links and sparsity of the true graph.
## S3 method for class 'sim' print( x, ... )
x |
An object of |
... |
System reserved (no specific usage). |
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
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
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
# Generating multivariate normal data from a 'random' graph data.sim <- bdgraph.sim( n = 20, p = 10, vis = TRUE ) print( data.sim )