rmBayes-package: The 'rmBayes' package.
In rmBayes: Performing Bayesian Inference for Repeated-Measures Designs
rmBayes-package R Documentation
The 'rmBayes' package.
Description
Performing Bayesian Inference for Repeated-Measures Designs
A Bayesian credible interval is interpreted with respect to posterior probability,
and this interpretation is far more intuitive than that of a frequentist confidence interval.
However, standard highest-density intervals can be wide due to between-subjects variability
and tends to hide within-subjects effects, rendering its relationship with the Bayes factor
less clear in within-subjects (repeated-measures) designs.
This urgent issue can be addressed by using within-subjects intervals in within-subjects designs.
References
Heck, D. W. (2019). Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: A comment on Nathoo, Kilshaw, and Masson (2018). Journal of Mathematical Psychology, 88, 27–31.
Loftus, G. R., & Masson, M. E. J. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin & Review, 1, 476–490.
Nathoo, F. S., Kilshaw, R. E., & Masson, M. E. J. (2018). A better (Bayesian) interval estimate for within-subject designs. Journal of Mathematical Psychology, 86, 1–9.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56, 356–374.
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org
rmBayes documentation built on Sept. 14, 2022, 9:06 a.m.
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| rmBayes-package | R Documentation |
The 'rmBayes' package.
Description
Performing Bayesian Inference for Repeated-Measures Designs
A Bayesian credible interval is interpreted with respect to posterior probability, and this interpretation is far more intuitive than that of a frequentist confidence interval. However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subjects effects, rendering its relationship with the Bayes factor less clear in within-subjects (repeated-measures) designs. This urgent issue can be addressed by using within-subjects intervals in within-subjects designs.
References
Heck, D. W. (2019). Accounting for estimation uncertainty and shrinkage in Bayesian within-subject intervals: A comment on Nathoo, Kilshaw, and Masson (2018). Journal of Mathematical Psychology, 88, 27–31.
Loftus, G. R., & Masson, M. E. J. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin & Review, 1, 476–490.
Nathoo, F. S., Kilshaw, R. E., & Masson, M. E. J. (2018). A better (Bayesian) interval estimate for within-subject designs. Journal of Mathematical Psychology, 86, 1–9.
Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56, 356–374.
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org
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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