https://github.com/cran/BDgraph
Tip revision: b33395ea3345776d14733f32a62f1643ebf7699d authored by Abdolreza Mohammadi on 28 April 2017, 21:30:01 UTC
version 2.36
version 2.36
Tip revision: b33395e
print.sim.Rd
\name{print.sim}
\alias{print.sim}
\title{ Print function for \code{S3} class \code{"sim"} }
\description{ Prints the information about the type of data, the sample size, the graph type, the number of nodes, number of links and sparsity of the true graph. }
\usage{ \method{print}{sim}( x, ... ) }
\arguments{
\item{x}{ An object of \code{S3} class \code{"sim"}, from function \code{\link{bdgraph.sim}}. }
\item{\dots}{ System reserved (no specific usage). }
}
\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:1501.05108v2}
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}
}
\author{ Abdolreza Mohammadi and Ernst Wit }
\seealso{ \code{\link{bdgraph.sim}} }
\examples{
\dontrun{
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 20, p = 10, vis = TRUE )
print( data.sim )
}
}