[R-sig-ME] R-sig-mixed-models Digest, Vol 32, Issue 32

Highland Statistics Ltd. highstat at highstat.com
Fri Aug 28 22:12:38 CEST 2009

r-sig-mixed-models-request at r-project.org wrote:
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> Today's Topics:
>    1. longitudinal analysis using lmer? (Robert Terwilliger)
> ----------------------------------------------------------------------
> Message: 1
> Date: Fri, 28 Aug 2009 15:26:33 -0400
> From: Robert Terwilliger <raterwil at gmail.com>
> Subject: [R-sig-ME] longitudinal analysis using lmer?
> To: r-sig-mixed-models at r-project.org
> Message-ID:
> 	<e837fef90908281226j6e7dd590p9c5249a58156429e at mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
> Dear R mixed effects gurus,
> I have the following data below. Attached is a png graphic
> representing the data.
> I would like to run the following analysis:
> signal ~ age | subject.
> For your information (not statically relevant), the "signal" variable
> is from a functional MRI experiment.
> At issue is whether this analysis is valid using "lme". From the graph
> (and the table below), one can see that there are five subjects.
> However, each subject begins at a different age. Subject 1 begins at 8
> and goes to 12, while subject 5 begins at 14. From my study of
> longitudinal analysis, usually each subject begins at the same
> starting point, while these data have subjects beginning at different
> starting points (different ages).

What exactly do you have in mind with "valid using lme"? I assume age as 
covariate and subject as random intercept? Using subject as random 
effect would impose the compound correlation structure, and starting 
time is irrelevant in that case. But you would basically assume that the 
correlation  between observations of age 8-12 is the same as between 
12-18. Unless you mess around with random intercept and slope models. 
The other thing is that 5 subjects is on the low side, especially for 
random intercept and slope models. Better get more subjects!




Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7

2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.

3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer

Other books: http://www.highstat.com/books.htm

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