Abstract Details
Activity Number:
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397
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract #311589
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View Presentation
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Title:
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glmmplus: An R Package for Messy Longitudinal Data
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Author(s):
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Ben Ogorek*+ and Caitlin Hogan
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Companies:
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Google and Google
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Keywords:
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imputation ;
random effects ;
R ;
longitudinal ;
variable selection ;
missing data
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Abstract:
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In modeling tasks involving large longitudinal data sets, there is often the need for random effects, grouped predictor terms, missing data forgiveness, nonlinear link functions, and variable selection capabilities. Many existing R packages focus on one of these problems, but the separate sets of functionality do not always integrate seamlessly. The glmmplus package addresses this problem by offering a wrapper to trusted packages such as mice and lme4, and adding new functionality such as Fast False Selection Rate (FSR) control for both forward and backward selection. The result is a la carte functionality to the user for messy longitudinal data. An analysis is presented from the National Longitudinal Survey.
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Authors who are presenting talks have a * after their name.
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