plotroc | R Documentation |
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() )
pred |
upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It can be an object with |
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 |
cut |
number of cut points. |
smooth |
logical: for smoothing the ROC curve. |
calibrate |
If |
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 |
Reza Mohammadi a.mohammadi@uva.nl; Lucas Vogels l.f.o.vogels@uva.nl
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
roc
, pROC::plot.roc()
, pROC::auc()
, bdgraph
, bdgraph.mpl
, compare
## 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)
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