ArticleJournal of speech, language, and hearing research : JSLHR2019
Functional Logistic Mixed-Effects Models for Learning Curves From Longitudinal Binary Data.
Article in Journal of speech, language, and hearing research : JSLHR, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Capturing the Heterogeneity of Word Learners by Analyzing Persons.Behavioral sciences (Basel, Switzerland) · 2024Article
- Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults.Journal of the American Statistical Association · 2021Article
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4 authors.
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Abstract
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.
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