GLMMs to identify the pure seasonal effect in a repeated measurement

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