EJWagenmakers
commented
10 months ago Interesting, and open to including this when we have the resources , but we have some bigger fish to fry atm :-)
Open TarandeepKang opened 10 months ago
EJWagenmakers
commented
10 months ago Interesting, and open to including this when we have the resources , but we have some bigger fish to fry atm :-)
Description
No response
Purpose
Whenever one might use repeated measures ANOVA or a mixed model the cluster bootstrap seems to outperform, and they wild cluster bootstrap can be especially useful for heteroscedasticity violation
Use-case
No response
Is your feature request related to a problem?
No response
Is your feature request related to a JASP module?
No response
Describe the solution you would like
Current inference for small samples or clustered standard errors with assumption violations may be inadequate
Describe alternatives that you have considered
No response
Additional context
Cluster Bootstrap is described in Deen & de Rooij and Wild method in fwildclusterboot package. For more detail see:
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-Based Improvements for Inference with Clustered Errors. The Review of Economics and Statistics, 90(3), 414–427. Deen, M., & de Rooij, M. (2020). ClusterBootstrap: An R package for the analysis of hierarchical data using generalized linear models with the cluster bootstrap. Behavior Research Methods, 52(2), 572–590. https://doi.org/10.3758/s13428-019-01252-y Djogbenou, A. A., MacKinnon, J. G., & Nielsen, M. Ã. (2018). Asymptotic Theory And Wild Bootstrap Inference With Clustered Errors (1399). Economics Department, Queen’s University. https://ideas.repec.org//p/qed/wpaper/1399.html Fischer, A., Roodman, D., fragments), A. Z. (Author of included sandwich, fragments), N. G. (Contributor to included sandwich, fragments), S. K. (Contributor to included sandwich, fragments), L. B. (Author of included fixest, & Krantz, S. (2023). fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models (0.13.0) [Computer software]. https://cran.r-project.org/web/packages/fwildclusterboot/ MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023). Fast and reliable jackknife and bootstrap methods for cluster-robust inference. Journal of Applied Econometrics, 38(5), 671–694. https://doi.org/10.1002/jae.2969 Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. (2019). Fast and wild: Bootstrap inference in Stata using boottest. The Stata Journal, 19(1), 4–60. https://doi.org/10.1177/1536867X19830877
Or another, possibly more flexible implementation:
Loy, A., & Korobova, J. (2023). Bootstrapping Clustered Data in R using lmeresampler. The R Journal, 14(4), 103–120. https://doi.org/10.32614/RJ-2023-015
Apologies but I'm not exactly sure in which module these features should go, somewhere between regression, ANOVA and mixed models, perhaps?-