------------------------------ Message: 4 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 Message-ID: <CAFvg1=vdVbz28pw9B6GrOXNsnceXK3UgXksMDwJUOQ9PYoLK_g at mail.gmail.com> Content-Type: text/plain; charset="utf-8" Dear All. 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 necessary. 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 in R. Dennis Dennis....try glmmTMB (and use gau() or exp())....or R-INLA to implement GLM(M)s with correlation. Kind regards, Alain ?[[alternative HTML version deleted]] ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models ------------------------------ End of R-sig-mixed-models Digest, Vol 134, Issue 39 ***************************************************
Dr. Alain F. Zuur Highland Statistics Ltd. 9 St Clair Wynd AB41 6DZ Newburgh, UK Email: highstat at highstat.com URL: www.highstat.com And: NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, and Utrecht University, P.O. Box 59, 1790 AB Den Burg, Texel, The Netherlands Author of: 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).