[R-sig-ME] a GlmmTMB advice

[Ben Bolker]

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On 5/25/20 8:31 PM, Leida Dos Santos wrote:
> Thank very much Ben,
> >There are 19 different specimens<- I mean 19 different species 
> (specimens in portuguese, I am sorry!)


   OK.

>
> Ben I am concerned because after checking the data carefully, I have 
> noticed that some 13 of the 16 different species there are less than 
> 10 individuals per species. Would it be ok to run the Glmm anyways? I 
> am modelling the abundance as response variable and 
> predictor variables year/month (the month of sampling for each year), 
> species, climate data (tmax, tmin, precipitation, and species life 
> traits history (size, habitat, habit, etc).

    I don't see immediately why this would be a problem with the 
species-richness analysis.

    Are you running 19 (or 16, I can't tell how many species there 
really are) separate abundance analyses?  That's going to be very 
difficult if you have small numbers of individuals per species. Also, if 
you run 19 analyses with 8 covariates each, the chances of getting a lot 
of false positives/need for some kind of shrinkage or 
multiple-comparisons correction goes up.  (This is not really a mixed 
modeling question, more a generic question of what to do with relatively 
small, noisy ecological data sets ...)


>
> I am sorry for not being able to formulate my questions properly.
>
> Kind regards,
>
>
>
>
>
> *Leida Dos Santos*
> */BSc/**/,QTS,MSc,/**/PhD/*
> /_**_*IUCN SSC ASG*** Programme *Officer*/
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>
> On Mon, 25 May 2020 at 17:44, Ben Bolker <bbolker using gmail.com 
> <mailto:bbolker using gmail.com>> wrote:
>
>
>     On 5/20/20 11:19 AM, Leida Dos Santos wrote:
>     > Hello there, I was wondering if you help. I am still learning
>     how to work
>     > with GlmmTMB and I have fitted GlmmTMB before but for
>     categorical data . I
>     > currently am working on a paper and have a data set where I am
>     trying to
>     > fit a GlmmTMB. I want to show the effect climate data on species
>     richness
>     > and abundance . I have fitted the predictor response with
>     species "Richness
>     > Index" and another with "abundance ", and predictor variables
>     "climate
>     > data" such as tmax, tmin, precipitation (Richness index I
>     calculated using
>     > vegan package). I have fitted (site and Month) as random
>     intercept, because
>     > the data was collected with no consistency but random days and
>     month, and
>     > years (2010-2019).
>
>         does the Month variable include both month and year (e.g.
>     2010.April, 2018.May)?
>
>     >   There are 19 different specimens
>
>          Not sure what this means ...
>
>     >   and n= 467. All
>     > variables are numerical. #Global Model example: Abun_2<-
>     glmmTMB(Richness ~
>     > (tmin +ppt1 + tmax1 + tmax2 +tmin2+ ppt2 + Year)^2+ (1|Site/Month),
>     > data=Main_data, family="nbinom2"). However when I run this
>     model, I come
>     > across some warning messages:
>     >
>     > "Found more than one class "Matrix" in cache; using the first, from
>     > namespace 'Matrix'
>     > Also defined by ‘arkhe’
>
>         This is harmless is in this case (although it seems like a
>     questionable decision on the part of the arkhe package developers).
>
>     > Warning messages:
>     > _1: In glmmTMB(Abundance ~ (tmin + ppt + tmax2 + tmax + tmin2 +
>     ppt2 + :
>     > non-integer counts in a nbinom2 model
>     > 2: In fitTMB(TMBStruc) :
>     > Model convergence problem; non-positive-definite Hessian matrix. See
>     > vignette('troubleshooting')
>     > 3: In fitTMB(TMBStruc) :
>     > Model convergence problem; function evaluation limit reached without
>     > convergence (9). See vignette('troubleshooting')".
>     >
>     > I checked vignette, but the truth is, this is too advanced for
>     and I do not
>     > understand what it is saying:) I would love to have some
>     feedback with
>     > regards to the model. Additionally, should I use just count for
>     Richness
>     > instead of the Menhinick index for richness? Or should I use a
>     completely
>     > different model? I
>
>         The main issue here is that it almost never makes sense to use a
>     count-based model (nbinom1, nbinom2, Poisson) for data that are not
>     actual counts (i.e. integers).  I'm not going to weigh in on which
>     index/model you should use; there are long discussions about that,
>     and
>     you should decide as much on biological grounds (what question are
>     you
>     trying to answer?) as statistical grounds.  As long as the model
>     converges to a stable answer, and the assumptions of linearity and
>     homoscedasticity are reasonably well met, you should be OK
>     statistically.
>
>     >   would be very grateful for any feedback please:):) Thank
>     > you in advance. :x
>
>
>
>     > *Leida Dos Santos*
>     > *BSc**,QTS,MSc,**PhD*
>     > *IUCN SSC ASG Programme Officer*
>     > *ldossantos using amphibians.org <mailto:ldossantos using amphibians.org>
>     <ldossantos using amphibians.org <mailto:ldossantos using amphibians.org>>*
>     > *leidamphibian using gmail.com <mailto:leidamphibian using gmail.com>
>     <leidamphibian using gmail.com <mailto:leidamphibian using gmail.com>>*
>     > *@anfileida*
>     > *http://www.amphibians.org/ <http://www.amphibians.org/>*
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