Dear Denis,
Have a look at the lme() and nlme() functions, both in the nlme package.
You find more details in Pinheiro & Bates (2000).
A linear trend over time:
lme(Y ~ Year, random = ~1|Department/Person)
Contrasts between years:
lme(Y ~ factor(Year), random = ~1|Department/Person)
You might want to add a correlation structure:
lme(Y ~ Year, random = ~1|Department/Person, correlation =
CorAR1(form~Year))
HTH,
Thierry
PS R-sig-mixed-models is a better list for questions on longitudinal
data
@BOOK{PinheiroBates2000,
title = {Mixed-Effects Models in {S} and {S-Plus}},
publisher = {Springer},
year = {2000},
author = {Pinheiro, Jose C. and Bates, Douglas M.},
note = {{ISBN 0-387-98957-0}},
abstract = {A comprehensive guide to the use of the `nlme' package for
linear
and nonlinear mixed-effects models.},
orderinfo = {springer.txt},
publisherurl =
{http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-10129-22-2102
822-0,00.html?changeHeader=true}
}
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[email protected]
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
> -----Oorspronkelijk bericht-----
> Van: [email protected]
> [mailto:[email protected]] Namens [email protected]
> Verzonden: dinsdag 23 februari 2010 9:27
> Aan: [email protected]
> Onderwerp: [R] Longitudinal analysis: contrasting time points
>
> Hi everyone
>
> I have the following situation:
>
> In a longitudinal study, subjects fill out a questionnaire
> every year (repeated measurements over time). Also, the
> subjects are nested within departments. There is an
> intervention going on over time. The outcome variable is
> continuous. Now I'd like to analyse two things:
>
> 1. Is there a significant change over time? I think this is
> done by a mixed-effects model with time as an independent
> variable (also called growth-curves according to Jos W.R. Twisk 2006).
>
> 2. I want to build contrasts between the years (i.e., time
> points). Thus, I'd like to know which years are different
> from each other. Normally, I would do an ANOVA with a
> TukeyHSD-posthoc test, but I'm not sure how to do this with
> repeated measurements over time and a nested design. Could
> anybody help me on this?
>
> Thanks for any help.
>
> Regards,
> Denis Aydin
>
>
> References:
>
> Applied Multilevel Analysis: A Practical Guide. Jos WR Twisk.
> Cambridge University Press, UK 2006
>
> --------------------------------------------------------------
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> PLEASE do read the posting guide
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>
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