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\name{roc}
\alias{roc}
\title{ Build a ROC curve }
\description{
This function builds a ROC curve specifically for graph structure learning and returns a \dQuote{roc} object, a list of class
\dQuote{roc}. This object can be \code{prin}ted, \code{plot}ted, or
passed to the functions \code{\link[pROC:roc]{pROC::roc()}}, \code{\link[pROC:ci]{pROC::ci()}}, \code{\link[pROC:smooth.roc]{pROC::smooth.roc()}}
and \code{\link[pROC:coords]{pROC::coords()}}. Additionally, two \code{roc} objects can be compared with \code{\link[pROC:roc.test]{pROC::roc.test()}}.
This function is based on the \code{\link[pROC]{roc}} function of \code{R} package \code{pROC}.
}
\usage{
roc( pred, actual, auc = TRUE, smooth = FALSE, plot = FALSE, quiet = TRUE, ... )
}
\arguments{
\item{pred }{
adjacency matrix (or a vector) corresponding to an estimated graph.
It can be an object with \code{S3} class "\code{bdgraph}" from function \code{\link{bdgraph}}.
It can be an object of \code{S3} class \code{"ssgraph"}, from the function \code{\link[ssgraph:ssgraph]{ssgraph::ssgraph()}} of \code{R} package \code{\link[ssgraph:ssgraph]{ssgraph::ssgraph()}}.
It can be a \code{\link{numeric}} or \code{\link{ordered}} vector of the same length than \code{actual}, containing the predicted
value of each observation.
}
\item{actual}{ adjacency matrix (or a vector) corresponding to the actual graph structure in which \eqn{a_{ij}=1} if there is a link between notes \eqn{i}{i} and \eqn{j}{j}, otherwise \eqn{a_{ij}=0}.
It can be an object with \code{S3} class "\code{sim}" from function \code{\link{bdgraph.sim}}.
It can be an object with \code{S3} class \code{"graph"} from function \code{\link{graph.sim}}.
It can be a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases).
Only two classes can be used in a ROC curve. }
\item{smooth}{if TRUE, the ROC curve is passed to \code{\link{smooth}}
to be smoothed.
}
\item{auc}{compute the area under the curve (AUC)? If \code{TRUE}
(default), additional arguments can be passed to \code{\link[pROC:auc]{pROC::auc()}}.
}
\item{plot}{plot the ROC curve? If \code{TRUE}, additional
arguments can be passed to \code{\link[pROC:plot.roc]{pROC::plot.roc()}}.
}
\item{quiet}{if \code{TRUE}, turn off \code{\link{message}}s when \code{direction} and \code{levels} are auto-detected.
}
\item{\dots}{ further arguments to be passed to \code{\link[pROC:roc]{pROC::roc()}}. }
}
\value{
If the data contained any \code{NA} value and \code{na.rm=FALSE}, \code{NA} is
returned. Otherwise, if \code{smooth=FALSE}, a list of class
\dQuote{roc} with the following fields:
\item{auc}{if called with \code{auc=TRUE}, a numeric of class \dQuote{auc} as
defined in \code{\link[pROC:auc]{pROC::auc()}}.
}
\item{ci}{if called with \code{ci=TRUE}, a numeric of class \dQuote{ci} as
defined in \code{\link[pROC:ci]{pROC::ci()}}.
}
\item{response}{the response vector. Patients whose response is not
\code{\link{\%in\%}} \code{levels} are discarded. If \code{NA} values
were removed, a \code{na.action} attribute similar
to \code{\link{na.omit}} stores the row numbers.
}
\item{predictor}{predictor vector converted to numeric as used to build the ROC
curve. Patients whose response is not \code{\link{\%in\%}} \code{levels} are discarded. If
\code{NA} values were removed, a \code{na.action} attribute similar
to \code{\link{na.omit}} stores the row numbers.
}
\item{original.predictor, original.response}{response and predictor vectors as passed in argument.}
\item{levels}{levels of the response as defined in argument.}
\item{controls}{predictor values for the control observations.}
\item{cases}{predictor values for the cases.}
\item{percent}{if the sensitivities, specificities and AUC are
reported in percent, as defined in argument.
}
\item{direction}{direction of the comparison, as defined in argument.}
\item{fun.sesp}{function used to compute sensitivities and specificities.
Will be re-used in bootstrap operations.}
\item{sensitivities}{sensitivities defining the ROC curve.}
\item{specificities}{specificities defining the ROC curve.}
\item{thresholds}{thresholds at which the sensitivities and
specificities were computed. See below for details.
}
\item{call}{how the function was called. See \code{\link{match.call}} for
more details.
}
If \code{smooth=TRUE} a list of class \dQuote{smooth.roc} as returned
by \code{\link[pROC:smooth]{pROC::smooth()}}, with or without additional elements
\code{auc} and \code{ci} (according to the call).
}
\references{
Tom Fawcett (2006) ``An introduction to ROC analysis''. \emph{Pattern
Recognition Letters} \bold{27}, 861--874, \doi{10.1016/j.patrec.2005.10.010}
Xavier Robin, Natacha Turck, Alexandre Hainard, \emph{et al.}
(2011) ``pROC: an open-source package for R and S+ to analyze and
compare ROC curves''. \emph{BMC Bioinformatics}, \bold{7}, 77,
\doi{10.1186/1471-2105-12-77}.
}
\author{Reza Mohammadi \email{a.mohammadi@uva.nl}; Lucas Vogels \email{l.f.o.vogels@uva.nl}}
\seealso{ \code{\link{plotroc}}, \code{\link[pROC:plot.roc]{pROC::plot.roc()}}, \code{\link[pROC:auc]{pROC::auc()}}, \code{\link[pROC:print.roc]{pROC::print.roc()}}, \code{\link{bdgraph}}, \code{\link{bdgraph.mpl}}, \code{\link{compare}} }
\examples{
\dontrun{
set.seed( 5 )
# Generating multivariate normal data from a 'scale-free' graph
data.sim = bdgraph.sim( n = 200, p = 15, graph = "scale-free", vis = TRUE )
# Running BDMCMC algorithm
sample.bdmcmc = bdgraph( data = data.sim, algorithm = "bdmcmc", iter = 10000 )
# ROC curve for BDMCMC algorithm
roc.bdmcmc = BDgraph::roc( pred = sample.bdmcmc, actual = data.sim, plot = TRUE )
# Running RJMCMC algorithm
sample.rjmcmc = bdgraph( data = data.sim, algorithm = "rjmcmc", iter = 10000 )
# ROC curve for RJMCMC algorithm
roc.rjmcmc = BDgraph::roc( pred = sample.rjmcmc, actual = data.sim, plot = TRUE )
# ROC curve for both BDMCMC and RJMCMC algorithms
pROC::ggroc( list( BDMCMC = roc.bdmcmc, RJMCMC = roc.rjmcmc ) )
}
}
\keyword{structure learning}
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