[R-sig-ME] Longitudinal logistic regression with, continuous-time first-order autocorrelation structure

[Highland Statistics Ltd]

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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



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End of R-sig-mixed-models Digest, Vol 134, Issue 39
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-- 

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).