https://github.com/cran/BDgraph
Revision ae993cfddd1bf99f5fd650ba3ee6bee6829e1f36 authored by Reza Mohammadi on 20 May 2018, 10:52:56 UTC, committed by cran-robot on 20 May 2018, 10:52:56 UTC
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Tip revision: ae993cfddd1bf99f5fd650ba3ee6bee6829e1f36 authored by Reza Mohammadi on 20 May 2018, 10:52:56 UTC
version 2.50
version 2.50
Tip revision: ae993cf
summary.bdgraph.Rd
\name{summary.bdgraph}
\alias{summary.bdgraph}
\title{ Summary function for \code{S3} class \code{"bdgraph"} }
\description{
Provides a summary of the results for function \code{\link{bdgraph}}.
}
\usage{
\method{summary}{bdgraph}( object, round = 2, vis = TRUE, ... )
}
\arguments{
\item{object}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}. }
\item{round}{ A value for rounding all probabilities to the specified number of decimal places. }
\item{vis}{ Visualize the results. }
\item{\dots}{System reserved (no specific usage).}
}
\value{
\item{best.graph}{The adjacency matrix corresponding to the selected graph which has the highest posterior probability.}
\item{p_links}{An upper triangular matrix corresponding to the posterior probabilities of all possible links.}
\item{K_hat}{The estimated precision matrix.}
}
\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 preprint arXiv:1501.05108}
Dobra, A. and A. Mohammadi (2017). Loglinear Model Selection and Human Mobility, \emph{arXiv preprint arXiv:1711.02623}
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 preprint arXiv:1706.04416}
}
\author{ Reza Mohammadi and Ernst Wit }
\seealso{\code{\link{bdgraph}}}
\examples{
\dontrun{
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim )
summary( bdgraph.obj )
bdgraph.obj <- bdgraph( data = data.sim, save.all = TRUE )
summary( bdgraph.obj )
summary( bdgraph.obj, vis = FALSE )
}
}
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