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Wednesday, June 29 • 1:36pm - 1:54pm
Simulation and power analysis of generalized linear mixed models

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As computers have improved, so has the prevalence of simulation studies to explore implications for assumption violations and explore statistical power. The simglm package allows for flexible simulation of general(ized) linear mixed models (multilevel models) under cross-sectional or longitudinal frameworks. In addition, the package allows for different distributional assumptions to be made such as non-normal residuals and random effects, missing data, and serial correlation. A power analysis by simulation can also be conducted by specifying a model to be simulated and the number of replications. This package can be useful for instructors or students for courses involving the general(ized) linear mixed model, as well as researchers looking to conduct simulations exploring the impact of assumption violations. The focus of the presentation will be on showing how to use the package, including live demos of the varying inputs and outputs, with working code. In addition to the syntax, a Shiny application will be made to show how the features can be made accessible to students in the classroom that are unfamiliar with R. The Shiny application will also provide a nice use case for the package, a live vignette of sorts.

Moderators
avatar for Patricia  Martinkova

Patricia Martinkova

researcher, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
Fulbright alumna and 2013-2015 visiting research scholar with Center for Statistics and the Social Sciences and Department of Statistics, University of Washington.

Speakers
avatar for Brandon  LeBeau

Brandon LeBeau

Assistant Professor, University of Iowa


Wednesday June 29, 2016 1:36pm - 1:54pm
Econ 140 579 Serra Mall, Stanford, CA 94305

Attendees (103)