diagnostics: Diagnostics Function for MCMC

In bhrcr: Bayesian Hierarchical Regression on Clearance Rates in the Presence of Lag and Tail Phases

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.

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.

Value

the directory location under which all the output is saved.

Author(s)

Colin B. Fogarty <[e-mail]>, Saeed Sharifi-Malvajerdi <[e-mail]>, Feiyu Zhu <[e-mail]>

Examples

data("posterior")
diagnostics(posterior)


data("pursat")
data("pursat_covariates")
out <- clearanceEstimatorBayes(data = pursat, covariates = pursat_covariates,
                               niteration = 200, burnin = 50, thin = 10)
diagnostics(out)

bhrcr documentation built on May 1, 2019, 8:41 p.m.