Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm

check_cdt_samples_convergence Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm

check_cdt_samples_convergence(cdt_samples)

Arguments

cdt_samples

the @sample slot of a cd.fit.mcmc S4 object (see package coarseDataTools)

Value

TRUE if the Gelman Rubin test for convergence was successful, FALSE otherwise

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.

See also

estimate_R

Author

Anne Cori

Examples

if (FALSE) { # \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

} # }