Description Usage Arguments Value Author(s) References See Also Examples
This function builds a ROC curve specifically for graph structure learning and returns a “roc” object, a list of class
“roc”. This object can be prin
ted, plot
ted, or
passed to the functions pROC::roc()
, pROC::ci()
, pROC::smooth.roc()
and pROC::coords()
. Additionally, two roc
objects can be compared with pROC::roc.test()
.
This function is based on the roc
function of R
package pROC
.
1 2 
pred 
An adjacency matrix corresponding to an estimated graph.
It can be an object with 
actual 
An adjacency matrix corresponding to the actual 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 
smooth 
if TRUE, the ROC curve is passed to 
auc 
compute the area under the curve (AUC)? If 
plot 
plot the ROC curve? If 
... 
further arguments to be passed to 
If the data contained any NA
value and na.rm=FALSE
, NA
is
returned. Otherwise, if smooth=FALSE
, a list of class
“roc” with the following fields:
auc 
if called with 
ci 
if called with 
response 
the response vector. Patients whose response is not

predictor 
the predictor vector converted to numeric as used to build the ROC
curve. Patients whose response is not 
original.predictor, original.response 
the response and predictor vectors as passed in argument. 
levels 
the levels of the response as defined in argument. 
controls 
the predictor values for the control observations. 
cases 
the predictor values for the cases. 
percent 
if the sensitivities, specificities and AUC are reported in percent, as defined in argument. 
direction 
the direction of the comparison, as defined in argument. 
fun.sesp 
the function used to compute sensitivities and specificities. Will be reused in bootstrap operations. 
sensitivities 
the sensitivities defining the ROC curve. 
specificities 
the specificities defining the ROC curve. 
thresholds 
the thresholds at which the sensitivities and specificities were computed. See below for details. 
call 
how the function was called. See 
If smooth=TRUE
a list of class “smooth.roc” as returned
by pROC::smooth()
, with or without additional elements
auc
and ci
(according to the call).
Reza Mohammadi a.mohammadi@uva.nl
Tom Fawcett (2006) “An introduction to ROC analysis”. Pattern Recognition Letters 27, 861–874. DOI: doi: 10.1016/j.patrec.2005.10.010.
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an opensource package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: doi: 10.1186/147121051277.
pROC::auc()
, pROC::plot.roc()
, pROC::print.roc()
, bdgraph
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## Not run:
set.seed( 100 )
# Generating multivariate normal data from a 'random' graph
data.sim < bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )
# Running sampling algorithm based on GGMs
sample.ggm < bdgraph( data = data.sim, method = "ggm", iter = 10000 )
# ROC curve for GGM method
roc.ggm < roc( pred = sample.ggm, actual = data.sim, plot = TRUE )
# Running sampling algorithm based on GCGMs
sample.gcgm < bdgraph( data = data.sim, method = "gcgm", iter = 10000 )
# ROC curve for GGM and GCGM methods
roc.gcgm < roc( pred = sample.gcgm, actual = data.sim, plot = TRUE )
ggroc( list( roc.ggm = roc.ggm, roc.gcgm = roc.gcgm ) )
## End(Not run)

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