BDgraph (version 2.70)

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( actual, pred, cut = 20, smooth = FALSE, linetype = NULL,  
         color = NULL, size = 1, main = "ROC Curve", 
         xlab = "False Postive Rate", ylab = "True Postive Rate",
         legend = TRUE, legend.size = 17, legend.position = c( 0.7, 0.3 ),
         labels = NULL, auc = TRUE, theme = ggplot2::theme_minimal() )

Arguments

actual

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.

pred

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.

linetype

specification for the default plotting line type.

color

specification for the default plotting color.

size

specification for the default plotting line size.

main

overall title for the plot.

xlab

title for the x axis.

ylab

title for the y axis.

legend

logical: for adding legend to the ROC plot.

legend.size

title for the x axis.

legend.position

title for the y axis.

labels

for legends of the legend to the ROC plot.

auc

logical: to report AUC with legend.

theme

theme for the plot from the function ggplot2::ggplot() of R package ggplot2::ggplot().

Author

Reza Mohammadi

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")

See Also

roc, pROC::plot.roc(), pROC::auc(), bdgraph, bdgraph.mpl, compare

Examples

if (FALSE) {
# To generate multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 30, p = 6, size = 7, vis = TRUE )
   
# To Run sampling algorithm
bdgraph.obj <- bdgraph( data = data.sim, iter = 10000 )

# To compare 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 )

# To Compare the resultss
plotroc( data.sim, list( bdgraph.obj, bdgraph.obj2 ), legend = FALSE )
}