regression

In this talk, I will look over the rationale for LMEMs, and demonstrate how to fit them in R (Brauer & Curtin, 2018; Luke, 2017). Challenges will also be covered. For instance, when using the widely-accepted ‘maximal’ approach, based on fitting all possible random effects for each fixed effect, models sometimes fail to find a solution, or ‘convergence’. Advice for the problem of nonconvergence will be demonstrated, based on the progressive lightening of the random effects structure (Singman & Kellen, 2017; for an alternative approach, especially with small samples, see Matuschek et al., 2017). At the end, on a different note, I will present a web application that facilitates data simulation for research and teaching (Bernabeu & Lynott, 2020).

This app presents linguistic data over several tabs. It combines the R Markdown-based user interface of Flexdashboard with a Shiny back-end that lets users download the data they select as CSV files and the plots as PNG images. One of the hardest nuts to crack was changing the orientation of rows and columns without breaking the reactable tables. Flexdashboard also made it possible to use quite different formats in different tabs.