Purpose
We present functional logistic mixed-effects models (FLMEMs) for estimating population and individual-level learning curves in longitudinal experiments.
Method
Using functional analysis tools in a Bayesian hierarchical framework, the FLMEM captures nonlinear, smoothly varying learning curves, appropriately accommodating uncertainty in various aspects of the analysis while also borrowing information across different model layers. An R package implementing our method is available as part of the
Supplemental Materials
.
Results
Application to speech learning data from
Reetzke, Xie, Llanos, and Chandrasekaran (2018)
and a simulation study demonstrate the utility of FLMEM and its many advantages over linear and logistic mixed-effects models.
Conclusion
The FLMEM is highly flexible and efficient in improving upon the practical limitations of linear models and logistic linear mixed-effects models. We expect the FLMEM to be a useful addition to the speech, language, and hearing scientist's toolkit.
Supplemental Material
https://doi.org/10.23641/asha.7822568