rhat: Rhat convergence diagnostic
In weiyaw/flexss: Fitting Flexible Subject-specific Curves
Description
Usage
Arguments
Value
References
Description
Compute Rhat convergence diagnostic as the maximum of rank normalized
split-Rhat and rank normalized folded-split-Rhat for one parameter.
Usage
1
rhat(sims)
Arguments
sims
A 2D array _without_ warmup samples (# iter * # chains).
Value
A single numeric value for the effective sample size.
References
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and
Paul-Christian Bürkner (2019). Rank-normalization, folding, and
localization: An improved R-hat for assessing convergence of
MCMC. arXiv preprint arXiv:1903.08008.
weiyaw/flexss documentation built on June 16, 2021, 7:48 a.m.
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Description Usage Arguments Value References
Description
Compute Rhat convergence diagnostic as the maximum of rank normalized split-Rhat and rank normalized folded-split-Rhat for one parameter.
Usage
1 | rhat(sims)
|
Arguments
sims |
A 2D array _without_ warmup samples (# iter * # chains). |
Value
A single numeric value for the effective sample size.
References
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and
Paul-Christian Bürkner (2019). Rank-normalization, folding, and
localization: An improved R-hat for assessing convergence of
MCMC. arXiv preprint arXiv:1903.08008.
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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