[R-sig-ME] R-sig-mixed-models Digest, Vol 81, Issue 50

[ONKELINX, Thierry]

Dear Xavier,

IMHO you should use the raw counts and the log of sampled volume as an offset. Then you are modelling densities in a correct way with e.g. a poisson or negative binomial family.

Be careful with stepwise regression. Search on the internet for 'stepwise regression' and 'harrell' and do some reading.

Best regards,

Thierry
________________________________________
Van: r-sig-mixed-models-bounces op r-project.org [r-sig-mixed-models-bounces op r-project.org] namens xavier chevillot [chevillot.xavier op live.fr]
Verzonden: maandag 30 september 2013 17:34
Aan: r-sig-mixed-models op r-project.org
Onderwerp: Re: [R-sig-ME] R-sig-mixed-models Digest, Vol 81, Issue 50

Thank you very much Mr Bolker to your answers

I still have some technicality points has clear!

I have understand that is easier to use a lmm with log transform data than a link log and a glmm. But my data are not exactly abundance of fish  but density of fish  by cubique meter. In the litterature we can read that with type of data we must use Gamma family (for continuous data) ( Yet Zuur said that you can use the poisson fammily because it is a count per volume (Mixed effects models and extentions in ecologiy with r))
So is it better to use a Gamma family with juste a inverse link or a poisson family with a log link or a log-data with gaussian family?

My data are times series of fish density it is for this that I use glmmPQL but can i use an other glmm function?

What is the equivalent function of StepAIC with glmm and if I use the glmmPQL how i can select my model (No AIC with PQL)?
Is it possible to select the variabes with a step AIC as un glm and after introduce this select variables in a glmm?

thank you very much again for the last answers i progress in my knowledges

Xavier

> >
> > Hello,
> > My name is Xavier ,
> > I have a problem with Glmm and I would like you to help me.
>
> > My main objective is to explain the inter-annual abundance [in]
> > variation of fish species with the environmental variables (as
> > temperature, salinity, flow) and biological variables (abundance of
> > different species of zooplankton) in the global warming contexte. I
> > have 4 species of fish who are sampling each month at the same
> > place,from 25 years. It is the same for the other variables.
>
> > I use this type of models
>
> > Mod1<-glmmPQL ( fish_1_abundance~ Temp+Salinity+Flow+zoo1+zoo2+zoo3,
>     random= 1|years, family=Gamma(link="log"),data=marin0,maxit=10000)
>
> First thought: do you really need a Gamma distribution with a log link?
> Especially if you're going to use a log link anyway, treating the
> data as log-Normal (i.e. log-transforming the data and then using
> a linear mixed model rather than a generalized linear m. m.) is
> often easier.
>
> > I have some questions?
> >
> > The first is, is that this model answers exactly to my question?
>
>    Hard to say, but it seems reasonable.  You probably want
> to account for seasonal variation somehow.  Is fish_1_abundance
> the abundance of species 1? You might want to fit a model with
> all species together, and interactions between species and the
> variables (i.e. Species*(var1+var2+var3+...), so you can test
> if the responses are different across species.
>
> Is there one sample per species per month, i.e. the total data set is
> 4*12*25 observations?
>
>
> > I want to know if the random effect 'years' take in count the
> > fact that there are good abundance years and not good?
>
>  yes, in principle
>
> > Is it possible to add in this model a correlation term like
> > correlation=corARMA(p=1,q=2,form=~1|years)
>
>   Yes, but (1) it would be much easier to interpret the meaning
> of the resulting model if you used lme() rather than glmmPQL
> (i.e. analyze data on the log scale as suggested above) and
> (2) you probably want to account for seasonality (as suggested
> above, and as I see your next question is ...)
>
> > How is it possible to take in count the seasonality of each variable?
>
>   You don't really need to account for the seasonality of the
> predictor variables.  You can add either a fixed or a random
> effect of month (although for the latter you probably need lme4::lmer,
> which will make the correlation modeling harder), or you can
> add sin(2*pi*season_prop) and cos(2*pi*season_prop) predictors,
> where season_prop ranges from 0 to 1, or you can use a periodic
> spline from the splines() package ...
>
> > Is it better to extract the tendency of explicative?s variables
>   before introduce in the model.
>
>  I think you mean "remove the trend", and no, not necessarily.
>
>
>
> ------------------------------
>

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