BDgraph (version 2.42)

plotroc: ROC plot

Description

Draws the ROC curve according to the true graph structure for object of S3 class "bdgraph", from function bdgraph.

Usage

plotroc( sim.obj, bdgraph.obj, bdgraph.obj2 = NULL, bdgraph.obj3 = NULL, 
         bdgraph.obj4 = NULL, cut = 20, smooth = FALSE, label = TRUE, 
         main = "ROC Curve" )

Arguments

sim.obj

An object of S3 class "sim", from function bdgraph.sim. It also can be the adjacency matrix corresponding to the true graph structure in which $a_{ij}=1$ if there is a link between notes $i$ and $j$, otherwise $a_{ij}=0$.

bdgraph.obj

An object of S3 class "bdgraph", from function bdgraph. It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.

bdgraph.obj2

An object of S3 class "bdgraph", from function bdgraph. It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links. It is for comparing two different approaches.

bdgraph.obj3

An object of S3 class "bdgraph", from function bdgraph. It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links. It is for comparing three different approaches.

bdgraph.obj4

An object of S3 class "bdgraph", from function bdgraph. It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links. It is for comparing four 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.

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

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 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" ) )   
# }