R: Posterior probabilities of the graphs

pgraph {BDgraph} | R Documentation

## Posterior probabilities of the graphs

### Description

Provides the estimated posterior probabilities for the most likely graphs or a specific graph.

### Usage

     pgraph( bdgraph.obj, number.g = 4, adj = NULL )

### Arguments

`bdgraph.obj` | An object of `S3` class `"bdgraph"`, from function `bdgraph`.
`number.g` | The number of graphs with the highest posterior probabilities to be shown. This option is ignored if `'adj'` is specified.
`adj` | An adjacency matrix corresponding to a graph structure. It is an upper triangular matrix in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0. It also can be an object of `S3` class `"sim"`, from function `bdgraph.sim`.

### Value

`selected_g` | the adjacency matrices which corresponding to the graphs with the highest posterior probabilities.
`prob_g` | A vector of the posterior probabilities of the graphs corresponding to `'selected\_g'`.

### Author(s)

Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit

### References

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

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2

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

### See Also

`bdgraph`, `bdgraph.mpl`

### Examples

    ## Not run:
    # Generating multivariate normal data from a 'random' graph
    data.sim <- bdgraph.sim( n = 50, p = 6, size = 6, vis = TRUE )

    bdgraph.obj <- bdgraph( data = data.sim, save = TRUE )

    # Estimated posterior probability of the true graph
    pgraph( bdgraph.obj, adj = data.sim )

    # Estimated posterior probability of first and second graphs with highest probabilities
    pgraph( bdgraph.obj, number.g = 2 )

    ## End(Not run)

[Package BDgraph version 2.62 Index]