BDgraph (version 2.25)

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 be

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

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

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

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 (2015). Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease, arXiv:1501.04849

See Also

bdgraph and compare

Examples

# 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 ) )