target.psrf: Calculates a Gelman Rubin diagnostic threshold using...

In stableGR: A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo

Calculates a Gelman Rubin diagnostic threshold using effective sample size thresholds.

Description

When the sample diagnostic reaches the psrf threshold calculated in this function, sufficient samples have been obtained.

Usage

target.psrf(p, m, epsilon = 0.05, delta = NULL, alpha = 0.05)

Arguments

p	dimension of the estimation problem.
m	number of chains.
epsilon	relative precision level. Values less than .10 are recommended.
delta	desired delta value - the cutoff for potential scale reduction factor. If specified, then the corresponding epsilon is returned.
alpha	significance level for confidence regions for the Monte Carlo estimators.

Value

psrf	The desired PSRF cutoff to stop the simulation.
epsilon	The epsilon value used to calculate the PSRF threshold.

References

Vats, D. and Knudson, C. Revisiting the Gelman-Rubin Diagnostic. arXiv:1812.09384

Vats, D. and Flegal, J. Lugsail lag windows and their application to MCMC. arXiv: 1809.04541.

Flegal, J. M. and Jones, G. L. (2010) Batch means and spectral variance estimators in Markov chain Monte Carlo. The Annals of Statistics, 38, 1034–1070.

Gelman, A and Rubin, DB (1992) Inference from iterative simulation using multiple sequences, Statistical Science, 7, 457-511.

Brooks, SP. and Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455.

Examples

target.psrf(p = 2, m = 3, epsilon = .05,  alpha = .05)

stableGR documentation built on Oct. 8, 2022, 1:05 a.m.