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

[Thierry Onkelinx]

Dear Tim,

lme4::glmer.nb(abundance ~ time + (1|SubplotID)) is as close as you can get
with lme4.

Best regards,

Thierry

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

2015-11-24 11:27 GMT+01:00 Tim Richter-Heitmann <trichter op uni-bremen.de>:

> 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 op uni-bremen.de <mailto:
>> 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
>>
>>     _______________________________________________
>>     R-sig-mixed-models op r-project.org
>>     <mailto:R-sig-mixed-models op r-project.org> mailing list
>>     https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>
> --
> Tim Richter-Heitmann (M.Sc.)
> PhD Candidate
>
>
>
> International Max-Planck Research School for Marine Microbiology
> University of Bremen
> Microbial Ecophysiology Group (AG Friedrich)
> FB02 - Biologie/Chemie
> Leobener Straße (NW2 A2130)
> D-28359 Bremen
> Tel.: 0049(0)421 218-63062
> Fax: 0049(0)421 218-63069
>
>

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