check_cdt_samples_convergence function

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)

Returns

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.

Examples

## Not run:

## 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
## End(Not run)

See Also

estimate_R

Author(s)

Anne Cori