R: ROC plot

plotroc {BDgraph}

R Documentation

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

Arguments

`target`	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`	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`	overall title for the plot.

Author(s)

Reza Mohammadi a.mohammadi@uva.nl

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

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, doi: 10.1214/18-AOAS1164

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, doi: 10.1111/rssc.12171

Examples

## 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)

[Package BDgraph version 2.65 Index]