bmksummary: Summaries of MCMC chains
In bmk: MCMC diagnostics package
Description
Usage
Arguments
References
Examples
Description
Generate the mean, standard deviation, median, 2.5
percent and 97.5 percent quantiles, Gelman Rubin
statistic for convergence, effective samples size and the
minimum and maximum Hellinger distances across all
chains. outputs summaries for the MCMC samples including
the convergence diagnostics of Gelman and Rubin and the
Hellinger distance of Boone, Merrick and Krachey.
Usage
1
bmksummary(inputlist)
Arguments
inputlist
A list of the combined MCMC chains for
all samples from one scenario.
References
Boone EL, Merrick JR and Krachey MJ. A Hellinger
distance approach to MCMC diagnostics. Journal of
Statistical Computation and Simulation,
DOI:10.1080/00949655.2012.729588.
Examples
1
2
data(MCMCsamples)
bmksummary(list( MCMC.one, MCMC.two, MCMC.three ))
bmk documentation built on May 1, 2019, 8:21 p.m.
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Description Usage Arguments References Examples
Description
Generate the mean, standard deviation, median, 2.5 percent and 97.5 percent quantiles, Gelman Rubin statistic for convergence, effective samples size and the minimum and maximum Hellinger distances across all chains. outputs summaries for the MCMC samples including the convergence diagnostics of Gelman and Rubin and the Hellinger distance of Boone, Merrick and Krachey.
Usage
1 | bmksummary(inputlist)
|
Arguments
inputlist |
A list of the combined MCMC chains for all samples from one scenario. |
References
Boone EL, Merrick JR and Krachey MJ. A Hellinger
distance approach to MCMC diagnostics. Journal of
Statistical Computation and Simulation,
DOI:10.1080/00949655.2012.729588.
Examples
1 2 | data(MCMCsamples)
bmksummary(list( MCMC.one, MCMC.two, MCMC.three ))
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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