zoib (version 1.6)

check.psrf: Convergence Check for Markov Chain Monte Carlo simulations via Potential Scale Reduction Factor

Description

check.psrf computes and summarizes univariate potential scale reduction factor. It also checks whether the multivariate potential scale reduction factor can be calculated.

Usage

check.psrf(post1 = NULL, post2 = NULL, post3 = NULL, post4 = NULL, post5 = NULL)

Value

The function outputs

psrf.s

univaraite psrf values and the 95% confidence interval from all model parameters.

psrf.m

multivariate psrf if the covariance matrix of the parameters are positive definite.

psrf.s.summ

the summary of the univariate psrf across parameter.

Arguments

post1

an mcmc.list with posterior samples from all Markov chains, or a data frame containing the draws from the 1st Markov Chain.

post2

Monte Carlo Posterior draws (data frame) from the 2nd Markov Chain, if the post1 is a data frame; specify post2 only needed

post3

Monte Carlo Posterior draws (data frame) from the 3rd Markov Chain, if the post1 is a data frame; specify post3 only needed

post4

Monte Carlo Posterior draws (data frame) from the 4th Markov Chain, if the post1 is a data frame; specify post4 only needed

post5

Monte Carlo Posterior draws (data frame) from the 5th Markov Chain, if the post1 is a data frame; specify post5 only needed

Author

Fang Liu ([e-mail])

Details

The posterior samples from each chain are stored in a data frame, with columns representing parameters from the model, and rows presenting posterior draws on the parameters. If the input post1 is a data frame contains the draws from one chain, then check.psrf can take up to 5 chains though it is not necessary to have 5 chain; but at least 2 chains are necessary.

Examples

  if (FALSE) {
	post1= data.frame(cbind(rnorm(400,0,1), rbeta(400,2,3)))
	post2= data.frame(cbind(rnorm(400,0,1), rbeta(400,2,3)))
	check.psrf(post1,post2)
	}