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
plotcoda.Rd
\name{plotcoda}
\alias{plotcoda}
\title{ Convergence plot }
\description{
Visualizes the cumulative occupancy fractions of all possible links in the graph.
It can be used for monitoring the convergence of the sampling algorithms, BDMCMC and RJMCMC.
}
\usage{ plotcoda( bdgraph.obj, thin = NULL, control = TRUE, main = NULL,
verbose = TRUE, ... )
}
\arguments{
\item{bdgraph.obj}{ object of \code{S3} class "\code{bdgraph}", from function \code{\link{bdgraph}}.
It also can be an object of \code{S3} class \code{"ssgraph"}, from the function \code{\link[ssgraph:ssgraph]{ssgraph::ssgraph()}} of \code{R} package \code{\link[ssgraph:ssgraph]{ssgraph::ssgraph()}}.
}
\item{thin}{ option for getting fast result for a cumulative plot according to part of the iteration.}
\item{control}{ logical: if TRUE (default) and the number of nodes is greater than 15, then 100 links randomly is selected for visualization. }
\item{main}{ graphical parameter (see plot). }
\item{verbose}{ logical: if TRUE (default), report/print the calculation progress. }
\item{\dots}{ system reserved (no specific usage). }
}
\details{
Note that a spending time for this function depends on the number of nodes.
%It should be slow for high-dimensional graphs.
For fast result, you can choose bigger value for the \code{'thin'} option.
}
\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} }
\seealso{ \code{\link{bdgraph}}, \code{\link{bdgraph.mpl}}, \code{\link{traceplot}} }
\examples{
\dontrun{
# Generating multivariate normal data from a 'circle' graph
data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim, iter = 10000, burnin = 0 , save = TRUE )
plotcoda( bdgraph.obj )
}
}
\keyword{hplot}