https://github.com/cran/BDgraph
Tip revision: 63357195b824666a99b078382a45f0744eca6b1c authored by Reza Mohammadi on 03 May 2021, 03:50:12 UTC
version 2.64
version 2.64
Tip revision: 6335719
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, 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
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, \emph{Bayesian Analysis}, 10(1):109-138
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, \emph{Annals of Applied Statistics}, 12(2):815-845
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}, 66(3):629-645
Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, \emph{arXiv preprint arXiv:1706.04416v2}
}
\author{ Reza Mohammadi \email{a.mohammadi@uva.nl} and Ernst Wit }
\seealso{ \code{\link{graph.sim}}, \code{\link{bdgraph.sim}} }
\examples{
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 20, p = 10, vis = TRUE )
print( data.sim )
}
\keyword{print}