plotroc function

ROC plot

Draws the receiver operating characteristic (ROC) curve according to the true graph structure for object of S3 class "bdgraph", from function bdgraph.

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().

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. (2023). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Author(s)

Reza Mohammadi a.mohammadi@uva.nl ; Lucas Vogels l.f.o.vogels@uva.nl

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)