effectsize (version 0.2.0)

interpret_ess: Bayesian indices interpretation

Description

Interpretation of Bayesian indices, such as Effective Sample Size (ESS), Rhat, or percentage in ROPE.

Usage

interpret_ess(ess, rules = "burkner2017")
interpret_rhat(rhat, rules = "vehtari2019")
interpret_rope(rope, ci = 0.9, rules = "default")

Arguments

ess

Value or vector of Effective Sample Size (ESS) values.

rules

A character string (see details) or a custom set of rules.

rhat

Value or vector of Rhat values.

rope

Value or vector of percentages in ROPE.

ci

The Credible Interval (CI) probability, corresponding to the proportion of HDI, that was used. Can be 1 in the case of "full ROPE".

Details

Rules sets:

• ESS: Can be "burkner2017" (default).

• Rhat: Can be "vehtari2019" (default) or "gelman1992".

• ROPE: Can be "default".

• ESS:

References

• B<U+00FC>rkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28.

• Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.

• Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & B<U+00FC>rkner, P. C. (2019). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.

• BayestestR's reporting guidelines

Examples

# NOT RUN {
interpret_ess(1001)
interpret_ess(c(852, 1200))

interpret_rhat(1.00)
interpret_rhat(c(1.5, 0.9))

interpret_rope(0, ci = 0.9)
interpret_rope(c(0.005, 0.99), ci = 1)
# }