bhrcr (version 1.0.3)

diagnostics: Diagnostics Function for MCMC

Description

diagnostics provides diagnostic analysis for the MCMC process used in the main function clearanceEstimatiorBayes.

Usage

diagnostics(object, ...)

Arguments

object

an object of class bhrcr, given by clearanceEstimatorBayes.

...

additional parameters.

Value

the directory location under which all the output is saved.

Details

This function provides diagnostic analysis such as trace plots, ACF and PACF plots for some important parameters in the simulation process of Gibbs sampling. With these diagnostic plots, we can be assured that we get the results after we have reached stationarity and have thinned sufficiently.

Examples

# NOT RUN {
data("posterior")
diagnostics(posterior)
# }
# NOT RUN {
data("pursat")
data("pursat_covariates")
out <- clearanceEstimatorBayes(data = pursat, covariates = pursat_covariates,
                               niteration = 200, burnin = 50, thin = 10)
diagnostics(out)
# }