[R-sig-ME] Longitudinal logistic regression with, continuous-time first-order autocorrelation structure
Highland Statistics Ltd
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Tue Feb 27 08:04:59 CET 2018
Date: Mon, 26 Feb 2018 22:22:17 -0800
From: Dennis Ruenger <dennis.ruenger at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Longitudinal logistic regression with
continuous-time first-order autocorrelation structure
<CAFvg1=vdVbz28pw9B6GrOXNsnceXK3UgXksMDwJUOQ9PYoLK_g at mail.gmail.com>
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I need to analyze an intensive longitudinal data set with a binary outcome
variable. In the “Ecological Momentary Assessment” (EMA) study,
participants received five random prompts per day for six weeks, asking
them (among other things) whether they were craving a particular drug
(yes/no). At the most basic level, I want to know whether the likelihood of
craving the drug changed across time.
Given the variable time intervals of measurement and many missing data
points, a continuous-time first-order autocorrelation model seems
I found tutorials on how to allow for continuous-time autocorrelation and
missing data in an LMM, using nlme::lme and corCAR1, but I am at a loss as
to what to do in a GLMM.
I would be thankful for any suggestions on how to analyze this kind of data
Dennis....try glmmTMB (and use gau() or exp())....or R-INLA to implement GLM(M)s with correlation.
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1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).
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