plotroc: ROC plot In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Description

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

Usage

 1 2 3 plotroc( target, est, est2 = NULL, est3 = NULL, est4 = NULL, cut = 20, smooth = FALSE, label = TRUE, main = "ROC Curve" )

Arguments

 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 S3 class "sim" from function bdgraph.sim. It can be an object with S3 class "graph" from function graph.sim. est, est2, est3, est4 An 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. label Logical: for adding legend to the ROC plot. main An overall title for the plot.

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

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

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ## 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)

Example output   10000 iteration is started.
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10000 iteration is started.
Iteration  1000
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Iteration  10000

BDgraph documentation built on May 3, 2021, 9:08 a.m.