https://github.com/cran/BDgraph
Tip revision: 794ac65f28f9d1174ad21a3cb2dc7c06c5fcf91b authored by Reza Mohammadi on 04 May 2018, 10:05:33 UTC
version 2.48
version 2.48
Tip revision: 794ac65
select.Rd
\name{select}
\alias{select}
\title{ Graph selection }
\description{
Provides the selected graph which, based on input, could be a graph with links for which their estimated posterior probabilities are greater than 0.5 (default)
or a graph with the highest posterior probability; see examples.
%For more specific selection of graphs consult the 'prob' function.
}
\usage{ select( bdgraph.obj, cut = NULL, vis = FALSE ) }
\arguments{
\item{bdgraph.obj}{ An object of \code{S3} class \code{"bdgraph"}, from function \code{\link{bdgraph}}. }
\item{cut}{ Threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; see the examples. }
\item{vis}{ Visualize the selected graph structure. }
}
\value{ \item{G}{ An adjacency matrix corresponding to the selected graph.} }
\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 )
select( bdgraph.obj )
bdgraph.obj <- bdgraph( data = data.sim, save.all = TRUE )
select( bdgraph.obj )
select( bdgraph.obj, cut = 0.5, vis = TRUE )
}
}