esr.mcmcrs: Effective Sampling Rate

In poissonconsulting/mcmcr: Manipulate MCMC Samples

Effective Sampling Rate

Description

Calculates the effective sampling rate (esr).

Usage

## S3 method for class 'mcmcrs'
esr(x, by = "all", as_df = FALSE, na_rm = FALSE, ...)

Arguments

x	An object.
by	A string indicating whether to determine by "term", "parameter" or "all".
as_df	A flag indicating whether to return the results as a data frame versus a named list.
na_rm	A flag specifying whether to ignore missing values.
...	Other arguments passed to methods.

Details

By default

\frac{1}{1 + 2 \sum_{k = 1}^\infty\rho_k(\theta)}

from Brooks et al. (2011) where the infinite sum is truncated at lag k when \rho_{k+1}(\theta) < 0.

Value

A number between 0 and 1 indicating the esr value.

References

Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.

See Also

Other convergence: converged_pars(), converged_terms(), converged(), esr_pars(), esr_terms(), rhat_pars(), rhat_terms(), rhat()

Examples

esr(mcmcrs(mcmcr_example, mcmcr_example))

poissonconsulting/mcmcr documentation built on Jan. 18, 2024, 1:11 a.m.