RESI is an R package designed to implement the Robust Effect Size Index (RESI, denoted as S) described in Vandekar, Tao, & Blume (2020). The RESI is a versatile effect size measure that can be easily computed and added to common reports (such as summary and ANOVA tables). This package currently supports lm, glm, nls, survreg, coxph, hurdle, zeroinfl, gee, geeglm, lme, and lmerMod models. Nonparametric bootstrapping is used to compute confidence intervals, although the interval performance has not yet been evaluated for the longitudinal models. A Bayesian bootstrap is also available for lm and nls models. In addition to the main resi function, the package also includes a point-estimate-only function (resi_pe), conversions from S to other common effect size measures and vice versa, print methods, plot methods, summary methods, and Anova/anova methods. A more detailed vignette is being written.
If you would like to contribute to the package, please branch off of our GitHub and submit a pull request describing the contribution. Please use the GitHub Issues page to report any problems and the Discussions page to seek additional support.
Jones, M., Kang, K., & Vandekar, S. (2023). RESI: An R Package for Robust Effect Sizes. arXiv preprint arXiv:2302.12345.
Kang, K., Jones, M. T., Armstrong, K., Avery, S., McHugo, M., Heckers, S., & Vandekar, S. Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices. Psychometrika. 2023. 10.1007/s11336-022-09899-x. Advance online publication. https://doi.org/10.1007/s11336-022-09899-x.
Vandekar S, Tao R, Blume J. A Robust Effect Size Index. Psychometrika. 2020 Mar;85(1):232-246. doi: 10.1007/s11336-020-09698-2.