plotroc {BDgraph}

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( pred, actual, cut = 200, smooth = FALSE, calibrate = TRUE, 
         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

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.

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.

cut

number of cut points.

smooth

logical: for smoothing the ROC curve.

calibrate

If TRUE, compute the value of AUC by taking the level of the probabilities into account.

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(s)

Reza Mohammadi [e-mail]; Lucas Vogels [e-mail]

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, 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, 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, doi:10.1080/01621459.2021.1996377

See Also

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

Examples

## Not run: 
# 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( bdgraph.ob2j, data.sim )
   
# To compare the results based on CGGMs approach
bdgraph.obj2 <- bdgraph( data = data.sim, method = "gcgm", iter = 10000 )

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

## End(Not run)