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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_cdt_samples_convergence.R
\name{check_cdt_samples_convergence}
\alias{check_cdt_samples_convergence}
\title{Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm}
\usage{
check_cdt_samples_convergence(cdt_samples)
}
\arguments{
\item{cdt_samples}{the \code{@sample} slot of a \code{cd.fit.mcmc} S4 object
(see package \code{coarseDataTools})}
}
\value{
TRUE if the Gelman Rubin test for convergence was successful, FALSE
otherwise
}
\description{
\code{check_cdt_samples_convergence} Checking convergence of an MCMC chain by
using the Gelman-Rubin algorithm
}
\details{
{
This function splits an MCMC chain in two halves and uses the Gelman-Rubin
algorithm to assess convergence of the chain by comparing its two halves.
}
}
\examples{
\dontrun{
## Note the following examples use an MCMC routine
## to estimate the serial interval distribution from data,
## so they may take a few minutes to run
## load data on rotavirus
data("MockRotavirus")
## estimate the serial interval from data
SI_fit <- coarseDataTools::dic.fit.mcmc(dat = MockRotavirus$si_data,
dist="G",
init_pars=init_mcmc_params(MockRotavirus$si_data, "G"),
burnin = 1000,
n.samples = 5000)
## use check_cdt_samples_convergence to check convergence
converg_diag <- check_cdt_samples_convergence(SI_fit@samples)
converg_diag
}
}
\seealso{
\code{\link{estimate_R}}
}
\author{
Anne Cori
}
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