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

[Thierry Onkelinx]

Dear Tim,

Have a look at the INLA package (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 op 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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>

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