Revision 9895d06622cc75a24d2b055040302cd3af7355ea authored by Abdolreza Mohammadi on 21 June 2017, 13:59:00 UTC, committed by cran-robot on 21 June 2017, 13:59:00 UTC
1 parent 1323820
plotroc.Rd
\name{plotroc}
\alias{plotroc}
\title{ ROC plot }
\description{
Draws the ROC curve according to the true graph structure for object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}.
}
\usage{
plotroc( sim.obj, bdgraph.obj, bdgraph.obj2 = NULL, bdgraph.obj3 = NULL,
bdgraph.obj4 = NULL, cut = 20, smooth = FALSE, label = TRUE,
main = "ROC Curve" )
}
\arguments{
\item{sim.obj}{
An object of \code{S3} class \code{"sim"}, from function \code{\link{bdgraph.sim}}.
It also can be the adjacency matrix corresponding to the true 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}.
}
\item{bdgraph.obj}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}.
It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links. }
\item{bdgraph.obj2}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}.
It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It is for comparing two different approaches. }
\item{bdgraph.obj3}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}.
It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It is for comparing three different approaches. }
\item{bdgraph.obj4}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}.
It also can be an upper triangular matrix corresponding to the estimated posterior probabilities for all possible links.
It is for comparing four different approaches. }
\item{cut}{ Number of cut points.}
\item{smooth}{ Logical: for smoothing the ROC curve.}
\item{label}{ Logical: for adding legend to the ROC plot.}
\item{main}{ An overall title for the plot.}
}
\references{
Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, \emph{Bayesian Analysis}, 10(1):109-138
Mohammadi, A. and E. Wit (2015). \pkg{BDgraph}: An \code{R} Package for Bayesian Structure Learning in Graphical Models, \emph{arXiv:1501.05108}
Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, \emph{Journal of the Royal Statistical Society: Series C}
Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, \emph{arXiv:1706.04416}
}
\author{ Abdolreza Mohammadi and Ernst Wit }
\seealso{\code{\link{bdgraph}} and \code{\link{compare}}}
\examples{
\dontrun{
# 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" ) )
}
}
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