prob: Posterior probabilities of the graphs

Description

According to the output of the BD-MCMC algorithm, this function gives the posterior probabilities for the 'g' most likely graphs or a specific graph, 'G'.

Usage

prob( output, g = 4, G = NULL )

Arguments

output

An object with S3 class "bdgraph".

The number of graphs with the highest probabilities to be shown (default is 4). This option is ignored if 'G' is specified.

Adjacency matrix corresponding to a graph structure. It is an upper triangular matrix in which $G_{ij}=1$ if there is a link between notes $i$ and $j$, otherwise $G_{ij}=0$. It also can be an object with S3 class "simulate".

Value

best.GThe graphs with the highest posterior probabilities.
prob.GA vector which includes posterior probabilities of the graphs in 'best.G'.

References

Mohammadi, A. and Wit, E. C. (2014). Bayesian structure learning in sparse Gaussian graphical models, Bayesian Analysis, acceped. http://arxiv.org/abs/1210.5371v6

Examples

Run this code

# generating synthetic multivariate normal data from a 'random' graph
  data.sim <- bdgraph.sim( n = 50, p = 6, size = 6, vis = TRUE )
  
  output   <- bdgraph( data = data.sim )
 
  # Estimated posterior probability of the true graph
  prob( output, G = data.sim )
  
  # Estimated posterior probability of the first and second graphs with highest probabilities
  prob( output, g = 2 )