BDgraph (version 2.33)

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,
                 cut.num = 20, smooth = FALSE, label = TRUE )

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.

cut.num

Number of cut points. The default value is 20.

smooth

Logical: for smoothing the ROC curve. The default is FALSE.

label

Logical: for adding legend to the ROC plot. The default is TRUE.

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:1501.05108 Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

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( 1, 4 ) )   
## ---------------------------------------------