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Introduction to Linear Mixed Effects Models
This workshop will describe and demo a pipeline in R to implement all aspects of dealing with data from repeated measures experimental designs - from specifying the design and model assumptions, to testing these assumptions, identifying outlier observations and outlier groups of repeated measures, sample-size or power calculations to help design follow-up experiments and finally deriving and visualizing estimates of interest. This workshop is a followup to the Introduction to Linear Mixed Effects Models workshop.
We will go over an R package, RMeDPower2 that our core developed. This is the website describing the tool. The package can be installed using the instructions provided on the the README page of the github repo linked to above. RMeDPower2 is a package that provides complete functionality to analyze data coming from repeated measures experiments, i.e., where one has repeated measures from the same biological/independent units or samples. RMeDPower2 helps test the modeling assumptions one makes, identify outlier observations, outlier units at different levels of the design, estimates statistical power or performs sample size calculations, estimate parameters of interest and also to visualize the association being tested. The functionality is limited to testing associations of one predictor (continuous or categorical, e.g., disease status or brain pathology) along with one another covariate (e.g., gender status) with possible interaction between the two variable in the context of hierarchical or crossed experimental designs. Please feel free to use this package in your own work and give us feedback, ask questions etc.