Cloud Hosting

We do the model comparison using the the loo package (9, 10) for leave-one-out cross validation. For an alternative approach using the WAIC criteria (11) I suggest you read this post also published by TDS Editors.

Under this scheme, the models have very similar performance. In fact, the first model is slightly better for out-of-sample predictions. Accounting for variance did not help much in this particular case, where (perhaps) relying on informative priors can unlock the next step of scientific inference.

I would appreciate your comments or feedback letting me know if this journey was useful to you. If you want more quality content on data science and other topics, you might consider becoming a medium member.

In the future, you may find an updated version of this post on my GitHub site.

1.M. Bieber, J. Gronewold, A.-C. Scharf, M. K. Schuhmann, F. Langhauser, S. Hopp, S. Mencl, E. Geuss, J. Leinweber, J. Guthmann, T. R. Doeppner, C. Kleinschnitz, G. Stoll, P. Kraft, D. M. Hermann, Validity and Reliability of Neurological Scores in Mice Exposed to Middle Cerebral Artery Occlusion. Stroke. 50, 28752882 (2019).

2. P.-C. Brkner, M. Vuorre, Ordinal Regression Models in Psychology: A Tutorial. Advances in Methods and Practices in Psychological Science. 2, 77101 (2019).

3. G. Gigerenzer, Mindless statistics. The Journal of Socio-Economics. 33, 587606 (2004).

4. P.-C. Brkner, Brms: An r package for bayesian multilevel models using stan. 80 (2017), doi:10.18637/jss.v080.i01.

5. H. Wickham, M. Averick, J. Bryan, W. Chang, L. D. McGowan, R. Franois, G. Grolemund, A. Hayes, L. Henry, J. Hester, M. Kuhn, T. L. Pedersen, E. Miller, S. M. Bache, K. Mller, J. Ooms, D. Robinson, D. P. Seidel, V. Spinu, K. Takahashi, D. Vaughan, C. Wilke, K. Woo, H. Yutani, Welcome to the tidyverse. 4, 1686 (2019).

6. D. Makowski, M. S. Ben-Shachar, D. Ldecke, bayestestR: Describing effects and their uncertainty, existence and significance within the bayesian framework. 4, 1541 (2019).

7. R. V. Lenth, Emmeans: Estimated marginal means, aka least-squares means (2023) (available at https://CRAN.R-project.org/package=emmeans).

8. R. McElreath, Statistical rethinking (Chapman; Hall/CRC, 2020; http://dx.doi.org/10.1201/9780429029608).

9. A. Vehtari, J. Gabry, M. Magnusson, Y. Yao, P.-C. Brkner, T. Paananen, A. Gelman, Loo: Efficient leave-one-out cross-validation and WAIC for bayesian models (2022) (available at https://mc-stan.org/loo/).

10. A. Vehtari, A. Gelman, J. Gabry, Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27, 14131432 (2016).

11. A. Gelman, J. Hwang, A. Vehtari, Understanding predictive information criteria for Bayesian models. Statistics and Computing. 24, 9971016 (2013).

Read the original post:

Avoiding abuse and misuse of T-test and ANOVA: Regression for categorical responses - Towards Data Science