[R-sig-ME] GLMMs to identify the pure seasonal effect in a repeated measurement

[Tim Richter-Heitmann]

Dear Thierry,

many thanks for your answer. I checked the output of my models again, 
and the random term when time was both fixed and random, indeed was 
always almost zero.
I think, i should clarify my sampling design briefly.
The plot was subdivided in 30 subplots.
A subplot was subdivided into 12  sampling locations on a regular grid.
For each time point, a unique pair of 2 neighboring sampling locations 
were sampled.
Meaning, the x,y-coordinates are different for each sampling date, 
together they form a perfect grid with 360
points.
I can see using locationIDs, but technically they are not from the same 
exact location for each date;
which is why i liked the 'correlation' argument in the lme models, in 
which i could use x,y coordinates.

Is there a way to incorporate this into the glmer.nb model you have 
proposed?

Thank you very much!

Tim

On 24.11.2015 10:30, Thierry Onkelinx wrote:
> Dear Tim,
>
> Have a look at the INLA package (www.r-inla.org 
> <http://www.r-inla.org>). It allows you to model spatially correlated 
> random effects, temporally correlated random effects, use a negative 
> binomial distribution and specify linear combination (needed for the 
> posthoc tests). Downside: it's not for the faint of heart.
>
> Having time as factor both in the fixed and random part is useless. 
> See http://rpubs.com/INBOstats/both_fixed_random
>
> Assuming that you revisited the same locations, then a reasonable 
> simple model would be:
>
> fit <- lme4::glmer.nb(abundance ~ time + (1|locationID))
>
> pro:
> - negative binomial
> - repeated visits to the locations acknowledged
> - post hoc test of time via glht
>
> contra:
> - compound symmetry correlation for location instead of spatial 
> correlation
> - no temporal correlation
>
> Best regards,
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature 
> and Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> 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
>
> 2015-11-23 22:39 GMT+01:00 <trichter at uni-bremen.de 
> <mailto:trichter at uni-bremen.de>>:
>
>
>     Dear list,
>
>     i am very new to mixed models. My data encompasses species
>     composition matrices from six different time points with spatial
>     correlation structure. For each species, i want to know if there
>     is a pure effect by time, f.e. if abundance changes can be purely
>     explained by time alone.
>     I used to glht() with time being a simple factor (so not
>     accounting for the repetitive nature of my data), but this seems
>     inapprobiate/wrong. So, i am actually trying to do:
>
>     fit <- lme(fixed=abundance ~ time, random=~1|time, data,
>     correlation=corxxx(form=~x.pos + y.pos))
>
>     with time being a factor with 6 levels (a side question would be,
>     if it would be better to use "time" as.time?)
>
>     Because my data is actually negative binomially distributed, i was
>     advised to use glmmPQL, but this gives me only intercepts, no
>     significancies or ways to compare models by log likelihood or AIC.
>
>     The basic question is, if that syntax is correct? Because i have
>     seen many examples looking at interactions, but never anything
>     where the only fixed predictor is also random. I do get an output,
>     which i can interpret and which resembles what i can actually see
>     from boxplots.
>
>     The overarching question is, if there are post-hoc tests for
>     repeated measurements of spatially autocorrelated, non-normally
>     distributed data.
>
>     Thank you, Tim
>
>     _______________________________________________
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>
>


-- 
Tim Richter-Heitmann (M.Sc.)
PhD Candidate



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