plotroc {BDgraph} | 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( target, est, est2 = NULL, est3 = NULL, est4 = NULL, cut = 20, smooth = FALSE, label = TRUE, main = "ROC Curve" )
target |
An 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 |
est,
est2,
est3,
est4 |
An upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It can be an object with |
cut |
Number of cut points. |
smooth |
Logical: for smoothing the ROC curve. |
label |
Logical: for adding legend to the ROC plot. |
main |
An overall title for the plot. |
Reza Mohammadi a.mohammadi@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
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845
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
## Not run: # 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" ) ) ## End(Not run)