Resources

Follow-up self-paced tutorial on simulation of data analyses for advanced power analyses

• https://lmu-osc.github.io/Simulations-for-Advanced-Power-Analyses/

Hallgren 2013

The article suggested for getting familiarised with the topic prior to the session, i.e. Hallgren, A. K. (2013). Conducting simulation studies in the R programming environment. Tutorials in Quantitative Methods for Psychology, 9(2), 43–60, contains accompanying R scripts and CSV data files which you can peruse in the Hallgren2013 folder of this repository. It contains:

• Annotated R syntax file for Example 1: novel question.R.
  
• Annotated R syntax file for Example 2: power analysis.R.
  
• Annotated R syntax file for Example 3: bootstrapping.R.
  
• CSV dataset generated in Example 1, which is also used later in Example 2: novel_question_output.csv.
  
• CSV dataset used in Example 3: mediation_raw_data.csv.

Other articles

Depending on the type of simulation that would be useful for you, these articles may be of interest:

• Johnson, P. C. D., Barry, S. J. E., Ferguson, H. M., & Müller, P. (2015). Power analysis for generalized linear mixed models in ecology and evolution. Methods in Ecology and Evolution, 6(2), 133–142. https://doi.org/10.1111/2041-210X.12306

• Blanco, D., Schroter, S., Aldcroft, A., Moher, D., Boutron, I., Kirkham, J. J., & Cobo, E. (2020). Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial. BMJ Open, 10(5), e036799. https://doi.org/10.1136/bmjopen-2020-036799

  • In the “Power analysis” section, there is a simple example of a power simulation. R code is provided in the supplementary material.

• Muldoon, A. (2018). Getting started simulating data in R: some helpful functions and how to use them. https://aosmith.rbind.io/2018/08/29/getting-started-simulating-data/

  • This blog gives a great overview of how to start simulating more complex datasets, including step-by-step explanations of relevant R functions.

• Privé, F., Aschard, H., Ziyatdinov, A., & Blum, M. G. B. (2018). Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr. Bioinformatics, 34(16), 2781–2787. https://doi.org/10.1093/bioinformatics/bty185

• Rönnegård, L., McFarlane, S. E., Husby, A., Kawakami, T., Ellegren, H., & Qvarnström, A. (2016). Increasing the power of genome wide association studies in natural populations using repeated measures – evaluation and implementation. Methods in Ecology and Evolution, 7(7), 792–799. https://doi.org/10.1111/2041-210X.12535

• Dalpiaz, D. (2020). Applied Statistics with R, section “Simulating SLR” in the chapter “Simple Linear Regression”. https://daviddalpiaz.github.io/appliedstats/simple-linear-regression.html#simulating-slr

Use of R packages to run simulations

• lme4: Bolker, B. Simulation-based power analysis for mixed models in lme4. https://rpubs.com/bbolker/simpower

• simstudy: Goldfeld, K., & Wujciak-Jens, J. Simulating Study Data. https://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html

• faux: DeBruine, L. (2023). faux: Simulation for Factorial Designs. https://debruine.github.io/faux/

• simsem (SIMulated Structural Equation Modeling): Pornprasertmanit, S., Miller, P., Schoemann, A., & Jorgensen, T. Vignette. https://github.com/simsem/simsem/wiki/Vignette

• simglm: LeBeau, B. Tidy Simulation with simglm. https://cran.r-project.org/web/packages/simglm/vignettes/tidy_simulation.html

• powerlmm: Magnusson, K. (2018). New paper: The consequences of ignoring therapist effects in longitudinal data analysis. https://rpsychologist.com/therapists-effects-longitudinal

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