BDgraph (version 2.23)

plotroc: ROC plot

Description

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

Usage

plotroc( G, prob, prob2 = NULL, cut = 20, smooth = FALSE )

Arguments

G

The adjacency matrix corresponding to the true graph structure in which $g_{ij}=1$ if there is a link between notes $i$ and $j$, otherwise $g_{ij}=0$. It also can be an object of S3 class "sim", from function

prob

Upper triangular matrix which shows the stimated posterior probabilities for all possible links. It also can be an object of S3 class "bdgraph", from function bdgraph.

prob2

This option is for comparing two different estimations. Upper triangular matrix which shows the stimated posterior inclusion probabilities for all possible links. It also can be an object of S3 class "bdgraph", from function

cut

Number of cut points. The default value is 20.

smooth

Logical: for smoothing the ROC curve. The default is FALSE.

References

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Mohammadi, A. and E. Wit (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv:1501.05108

Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2015). Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease, arXiv:1501.04849

See Also

bdgraph and compare

Examples

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 30, p = 6, size = 7, vis = TRUE )
   
# Runing sampling algorithm
output <- bdgraph( data = data.sim, iter = 10000 )
# comparing the result
plotroc( data.sim, output )
   
# To compare the result based on CGGMs approach
output2 <- bdgraph( data = data.sim, method = "gcgm", iter = 10000 )
# Comparing the results
plotroc( data.sim, output, output2 )
legend( "bottomright", c("Gaussian", "Copula"), lty = c(1,2), col = c(1, 4))