plotroc: 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: 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: 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::ssgraph() of R package ssgraph::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: overall title for the plot.

Author

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, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, tools:::Rd_expr_doi("10.1214/14-BA889")

Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")

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, tools:::Rd_expr_doi("10.1111/rssc.12171")

Examples

Run this code

if (FALSE) {
# 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" ) )   
}