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Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. Two of these functions are deciles_interaction_plot and sextiles_interaction_plot. These functions allow the plotting of interactions between two continuous variables.

Frequentist and Bayesian statistics are sometimes regarded as fundamentally different philosophies. Indeed, can both qualify as philosophies or is one of them just a pointless ritual? Is frequentist statistics only about $p$ values? Are frequentist estimates diametrically opposed to Bayesian posterior distributions? Are confidence intervals and credible intervals irreconcilable? Will R crash if lmerTest and brms are simultaneously loaded?

This post presents a run-through of a Bayesian workflow in R. The content is closely based on Bernabeu (2022), which was in turn based on lots of other references, also cited here.

The function knit_deleting_service_files() helps avoid (R) Markdown knitting errors caused by files and folders remaining from previous knittings (e.g., manuscript.tex, ZHJhZnQtYXBhLlJtZA==.Rmd, manuscript.synctex.gz). The only obligatory argument for this function is the name of a .Rmd or .md file. The optional argument is a path to a directory containing this file. The function first offers szeleting potential service files and folders in the directory. A confirmation is required in the console (see screenshot below). Next, the document is knitted. Last, the function offers deleting potential service files and folders again.

As technology and research methods advance, the data sets tend to be larger and the methods more exhaustive. Consequently, the analyses take longer to run. This poses a challenge when the results are to be presented using R Markdown. One has to balance reproducibility and efficiency. On the one hand, it is desirable to keep the R Markdown document as self-contained as possible, so that those who may later examine the document can easily test and edit the code.

When knitting an R Markdown document after the first time, errors may sometimes appear. Three tips are recommended below.

1. Close PDF reader window When the document is knitted through the ‘Knit’ button, a PDF reader window opens to present the result. Closing this window can help resolve errors.
2. Delete service files Every time the Rmd is knitted, some service files are created. Some of these files have the ‘.

Three strategies for reducing computation time when using simr::powercurve with Kenward-Roger or Satterthwaite methods: using high-performance computing, parallelizing different effects, and parallelizing different sample sizes within the breaks argument.

Critical examination of Liu et al. (2018) claims about methodological inconsistencies in ERP studies of conceptual modality switching, arguing that their conclusions overlook theoretical and methodological justifications for varying analytical approaches.

Based on Michael Frank's presentation, three methods for collaborating on R Markdown documents: using GitHub for version control, copying text to trackable editors while preserving code, or knitting to Word/PDF formats.

Common punctuation pitfalls in formal writing, including run-on sentences, comma splices, and the appropriate use of the Oxford comma based on whether disambiguation is needed.