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resources.qmd
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# Resources
## Follow-up self-paced tutorial on simulation of data analyses for advanced power analyses
* <a href="https://lmu-osc.github.io/Simulations-for-Advanced-Power-Analyses/" target="_blank">https://lmu-osc.github.io/Simulations-for-Advanced-Power-Analyses/</a>
## 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](https://doi.org/10.20982/tqmp.09.2.p043), contains accompanying R scripts and CSV data files which you can peruse in the [Hallgren2013](https://github.com/lmu-osc/Introduction-Simulations-in-R/tree/main/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. <a href="https://doi.org/10.1111/2041-210X.12306" target="_blank">https://doi.org/10.1111/2041-210X.12306</a>
* 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. <a href="https://doi.org/10.1136/bmjopen-2020-036799" target="_blank">https://doi.org/10.1136/bmjopen-2020-036799</a>
* 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. <a href="https://aosmith.rbind.io/2018/08/29/getting-started-simulating-data/" target="_blank">https://aosmith.rbind.io/2018/08/29/getting-started-simulating-data/</a>
* 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. <a href="https://doi.org/10.1093/bioinformatics/bty185" target="_blank">https://doi.org/10.1093/bioinformatics/bty185</a>
* 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. <a href="https://doi.org/10.1111/2041-210X.12535" target="_blank">https://doi.org/10.1111/2041-210X.12535</a>
* Dalpiaz, D. (2020). *Applied Statistics with R*, section "Simulating SLR" in the chapter "**Simple Linear Regression**". <a href="https://daviddalpiaz.github.io/appliedstats/simple-linear-regression.html#simulating-slr" target="_blank">https://daviddalpiaz.github.io/appliedstats/simple-linear-regression.html#simulating-slr</a>
## Use of R packages to run simulations
* `lme4`: Bolker, B. Simulation-based power analysis for mixed models in `lme4`. <a href="https://rpubs.com/bbolker/simpower" target="_blank">https://rpubs.com/bbolker/simpower</a>
* `simstudy`: Goldfeld, K., & Wujciak-Jens, J. Simulating Study Data. <a href="https://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html" target="_blank">https://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html</a>
* `faux`: DeBruine, L. (2023). faux: Simulation for Factorial Designs. <a href="https://debruine.github.io/faux/" target="_blank">https://debruine.github.io/faux/</a>
* `simsem` (SIMulated Structural Equation Modeling): Pornprasertmanit, S., Miller, P., Schoemann, A., & Jorgensen, T. Vignette. <a href="https://github.com/simsem/simsem/wiki/Vignette" target="_blank">https://github.com/simsem/simsem/wiki/Vignette</a>
* `simglm`: LeBeau, B. Tidy Simulation with `simglm`. <a href="https://cran.r-project.org/web/packages/simglm/vignettes/tidy_simulation.html" target="_blank">https://cran.r-project.org/web/packages/simglm/vignettes/tidy_simulation.html</a>
* `powerlmm`: Magnusson, K. (2018). New paper: The consequences of ignoring therapist effects in longitudinal data analysis. <a href="https://rpsychologist.com/therapists-effects-longitudinal" target="_blank">https://rpsychologist.com/therapists-effects-longitudinal</a>
***