BDgraph (version 2.43)

plinks: Estimated posterior link probabilities

Description

Provides the estimated posterior link probabilities for all possible links in the graph.

Usage

plinks( bdgraph.obj, round = 2, burnin = NULL )

Arguments

bdgraph.obj

An object of S3 class "bdgraph", from function bdgraph.

round

A value for rounding all probabilities to the specified number of decimal places.

burnin

The number of burn-in iteration to scape.

Value

p_links

An upper triangular matrix which corresponds the estimated posterior probabilities for all possible links.

References

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Mohammadi, A. and E. Wit (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv preprint arXiv:1501.05108

Dobra, A. and A. Mohammadi (2017). Loglinear Model Selection and Human Mobility, arXiv preprint arXiv:1711.02623

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416

See Also

bdgraph and bdgraph.sim

Examples

# NOT RUN {
	
# }
# NOT RUN {
	# Generating multivariate normal data from a 'cycle' graph
	data.sim <- bdgraph.sim( n = 70, p = 6, graph = "cycle", vis = TRUE )
   
	bdgraph.obj   <- bdgraph( data = data.sim, iter = 10000 )
  
	plinks( bdgraph.obj, round = 2 )
	
# }