Abstract
We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.
Highlights
The most common techniques for calculating the correlation between two variables assume that each pair of data points arises from an independent observation
For example, a study that calculates the correlation between age and the volume of a specific brain region for a sample of people
We previously developed the rmcorr package[8] in R9 to make the repeated measures correlation technique widely available for researchers; it has since been adapted as a function in the Pingouin statistics package[10] for Python
Summary
2. Emmeke Aarts , Utrecht University, Utrecht, The Netherlands Sebastian Mildiner Moraga, Utrecht University, Utrecht, The Netherlands. Any reports and responses or comments on the article can be found at the end of the article. This article is included in the RPackage gateway
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