https://github.com/cran/BDgraph
Tip revision: 72b95efce3f7c808386f6e86b3789d004a213bf5 authored by Reza Mohammadi on 25 December 2022, 06:20:14 UTC
version 2.72
version 2.72
Tip revision: 72b95ef
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}{ object of \code{S3} class "\code{bdgraph}", from function \code{\link{bdgraph}}. }
\item{round}{ value for rounding all probabilities to the specified number of decimal places. }
\item{vis}{ visualize the results. }
\item{\dots}{ additional plotting parameters for the case \code{vis = TRUE}. See \code{\link[BDgraph]{plot.graph}}. }
}
\value{
\item{selected_g}{adjacency matrix corresponding to the selected graph which has the highest posterior probability.}
\item{p_links}{upper triangular matrix corresponding to the posterior probabilities of all possible links.}
\item{K_hat}{estimated precision matrix.}
}
\references{
Mohammadi, R. and Wit, E. C. (2019). \pkg{BDgraph}: An \code{R} Package for Bayesian Structure Learning in Graphical Models, \emph{Journal of Statistical Software}, 89(3):1-30, \doi{10.18637/jss.v089.i03}
}
\author{ Reza Mohammadi \email{a.mohammadi@uva.nl} and Ernst Wit }
\seealso{ \code{\link{bdgraph}}, \code{\link{bdgraph.mpl}} }
\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 = TRUE )
summary( bdgraph.obj )
summary( bdgraph.obj, vis = FALSE )
}
}