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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] Repeated measures within and across years
From:       Henrik Singmann <henrik.singmann () psychologie ! uni-freiburg ! de>
Date:       2013-09-26 9:41:14
Message-ID: 5244013A.1050609 () psychologie ! uni-freiburg ! de
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Hi Kristen,

Although I am no expert in neither count models nor ecology, I will step in with some \
thoughts as you haven't received any response so far.

One of the advantages of mixed models over the classical approaches is that it allows \
for replications within a cell of the design. That is, one can usually avoid \
aggregation (at least if there aren't too many zero cells, I guess).

That being said, in your case the simplest model would be:

count ~ year + (1|site)

A fixed effect for year plus a random effect for site that accounts for differences \
in overall levels of populations between sites (i.e., a site random intercept). This \
already accounts for the repeated measure nature of the data.

However, as often said on this list, it is probably a good idea to include random \
slopes for year as well, to allow the effect of year to also vary site specific:

count ~ year + (year|site)

Next, it might be a good idea to consider within year variations by also modeling the \
effect of when within a year the data is measured:

count ~ year + jdate + (year + jdate|site)

(I am not so sure if with only three observations per site and year the jdate random \
slope is estimated precise enough, but one needs to inspect the model afterwards.)

Finally, one could also have the idea that year and jdate interact and come to the \
largest model (for which the same caveat as above applies):

count ~ year*jdate + (year*jdate|site)

Either of those formulas should be put in a call to glmer(..., family = poisson). \
However, you will also have to consider the case of over- or underdispersion, but as \
said above. This is not my field, so I have no idea about it.

Any other model (e.g., including ID) seems inappropriate given my understanding of \
your data (or I don'T understand what ID is supposed to be).

Hope that helps,
Henrik


Am 24.09.2013 00:24, schrieb Kristen Dybala:
> Hi all,
> I'm hoping someone can help me wrap my head around an appropriate model
> structure for this situation, with repeated measures within a site during
> each year, which are then also repeated over many years. We have 14 sites,
> each surveyed ~3 times per year, repeated each year for ~13 years (with
> random missing data). The response variable is the number of individuals of
> one species observed per survey per site per year.
> 
> The data look like this:
> ID  site year  jdate  count
> 1    A   1999 150    5
> 2    A   1999 172    0
> 3    A   1999 185    3
> 4    A   2000 143    2
> 5    A   2000 162    1
> 
> [To really get into the nitty gritty, each survey actually consists of 3-5
> point counts, but for now at least these have been pooled. Also, we have
> done distance sampling analysis to be able to correct for detection
> probabilities with an offset.]
> 
> We're primarily interested in the long-term trend in abundance across
> sites, though eventually we would also like to compare trends between
> sites, which received different habitat restoration treatments.
> 
> For starters, my questions are:
> 1) Am I correct in believing I should use mixed modeling (and a random
> effect of site) because these data are repeated measures of each site over
> time?
> 
> 2) If so, do I also need to include an individual survey ID effect nested
> within site, because there are repeated measures per site per year? (How
> would I do this? Will that even work with only 3 surveys per site per
> year?) Or does a random effect of site encompass both the repeated measures
> across and within years?
> 
> 3) Does it make sense to consider including a crossed effect of year (as a
> factor) in addition to (or instead of) a random slope of year within site?
> What about year nested within site?
> 
> Any insights would be greatly appreciated.
> Thanks,
> Kristen
> 
> 
> ----------------------------------------------------------
> Kristen Dybala, Post-doctoral Researcher
> Museum of Wildlife and Fish Biology
> University of California, Davis
> kedybala@ucdavis.edu
> (415) 218-9295 - cell
> 
> 	[[alternative HTML version deleted]]
> 

-- 
Dipl. Psych. Henrik Singmann
PhD Student
Albert-Ludwigs-Universität Freiburg, Germany
http://www.psychologie.uni-freiburg.de/Members/singmann

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