Description Usage Arguments Author(s) References See Also Examples

Draws the receiver operating characteristic (ROC) curve according to the true graph structure for object of `S3`

class `"bdgraph"`

, from function `bdgraph`

.

1 2 3 |

`target` |
An adjacency matrix corresponding to the true graph structure in which |

```
est,
est2,
est3,
est4
``` |
An upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It can be an object with |

`cut ` |
Number of cut points. |

`smooth` |
Logical: for smoothing the ROC curve. |

`label ` |
Logical: for adding legend to the ROC plot. |

`main ` |
An overall title for the plot. |

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Not run:
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 30, p = 6, size = 7, vis = TRUE )
# Runing sampling algorithm
bdgraph.obj <- bdgraph( data = data.sim, iter = 10000 )
# Comparing the results
plotroc( data.sim, bdgraph.obj )
# To compare the results based on CGGMs approach
bdgraph.obj2 <- bdgraph( data = data.sim, method = "gcgm", iter = 10000 )
# Comparing the resultss
plotroc( data.sim, bdgraph.obj, bdgraph.obj2, label = FALSE )
legend( "bottomright", c( "GGMs", "GCGMs" ), lty = c( 1, 2 ), col = c( "black", "red" ) )
## End(Not run)
``` |

```
10000 iteration is started.
Iteration 1000
Iteration 2000
Iteration 3000
Iteration 4000
Iteration 5000
Iteration 6000
Iteration 7000
Iteration 8000
Iteration 9000
Iteration 10000
10000 iteration is started.
Iteration 1000
Iteration 2000
Iteration 3000
Iteration 4000
Iteration 5000
Iteration 6000
Iteration 7000
Iteration 8000
Iteration 9000
Iteration 10000
```

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