R: ROC plot

plotroc {BDgraph} | R Documentation

## ROC plot

### Description

Draws the receiver operating characteristic (ROC) curve according to the true graph structure for object of `S3` class `"bdgraph"`, from function `bdgraph`.

### Usage

    plotroc( target, est, est2 = NULL, est3 = NULL, est4 = NULL,
             cut = 20, smooth = FALSE, label = TRUE, main = "ROC Curve" )

### Arguments

`target` | An adjacency matrix corresponding to the true graph structure in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0. It can be an object with `S3` class `"sim"` from function `bdgraph.sim`. It can be an object with `S3` class `"graph"` from function `graph.sim`.
`est, est2, est3, est4` | An upper triangular matrix corresponding to the estimated posterior probabilities for all possible links. It can be an object with `S3` class `"bdgraph"` from function `bdgraph`. It can be an object of `S3` class `"ssgraph"`, from the function `ssgraph` of `R` package `ssgraph`. It can be an object of `S3` class `"select"`, from the function `huge.select` of `R` package `huge`. Options `est2, est3` and `est4` are for comparing two or more different approaches.
`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.

### Author(s)

Reza Mohammadi a.mohammadi@uva.nl

### 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

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

### See Also

`bdgraph`, `bdgraph.mpl`, `compare`

### Examples

    ## 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)

[Package BDgraph version 2.62 Index]