EpiEstim (version 2.2-4.1)

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

Description

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

Usage

check_cdt_samples_convergence(cdt_samples)

Value

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

Arguments

cdt_samples

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

Author

Anne Cori

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

Examples

if (FALSE) {
## 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

}