[R-sig-ME] Looking for help to test the effect of a grouping factor in repeated measures

[Mitchell Maltenfort]

https://stats.stackexchange.com/questions/79360/mixed-effects-model-with-nesting
might be helpful

On Thu, Sep 9, 2021 at 10:20 AM Thierry Onkelinx via R-sig-mixed-models <
r-sig-mixed-models using r-project.org> wrote:

> Dear Lea,
>
> Please note that you want the binomial distribution. Not the binomial
> _error_ distribution.
>
> A model like y ~ treatment * species + (1|date) could make sense if you can
> assume that the date could have a common effect on the results. E.g. the
> observer being more awake on some days ;-) Use the actual date as a factor
> instead of the number of days since the start of the experiment. That is
> safer than using the number of days since the start.
> If you assume no date effect, then there could be only temporal
> autocorrelation within the species. As the strength of the autocorrelation
> could be different among species, you could consider fitting a separate
> model for every species.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
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>
> ///////////////////////////////////////////////////////////////////////////////////////////
> 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
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op do 9 sep. 2021 om 15:43 schreef Léa Fieschi-Méric <
> leafieschimeric using gmail.com>:
>
> > Hello,
> >
> > I have a dataset to analyse and I am struggling to build my model set and
> > was wondering if you could advise me please.
> >
> > The experimental plan is as follows: several species of animals were
> > observed for a year, but not regularly (some months have many
> observations,
> > others have less). *All individuals belonging to the same species were
> > housed together in the same terrarium* (unequal number of individuals per
> > species). The response variable is the number of awake individuals per
> > terrarium (i.e. per species). The year was divided in 3, unequal, periods
> > as follows: treatment / control / treatment.
> >
> > I need to determine the effect of the *Period *(treatment / control), of
> > the *Species* (12 in total), and of their *Interaction *on the
> *proportion
> > of awake animals*. I wanted to use a GLMM with a binomial error
> > distribution. The problem is that I have repeated measures per species,
> so
> > I would like to account for that by using Species as a random effect to
> > avoid pseudoreplication, but I think I need Species to be a fixed effect
> > because I want to directly test its effect on my response. Therefore, I
> > wonder if adding the *Date* (as an integer: number of days since the
> > beginning of the experiment) to the model does control for these repeated
> > measures (but I can't use it as a random effect because it is continuous,
> > and it makes my model too complex to converge when integrated as a fixed
> > effect), or if I should sum the response per month to use *Month* as a
> > random effect ordered factor. Or if I could just use a linear model
> without
> > bothering, like this: y ~ Treatment * Species !
> > I am stuck here and would like to avoid taking a mathematically
> > erroneous approach. Could you advise me please?
> >
> > I was also wondering what I need to do in order to, in a second phase, be
> > able to conclude exactly which species were sensitive to the treatment
> and
> > which were not?
> >
> > I am looking forward to hearing back from you, and thank you in advance
> for
> > your help!
> >
> > Léa
> >
> > --
> > *Léa FIESCHI-MERIC*, PhD student
> >
> > Laboratoire d'Ecologie et de Conservation des Amphibiens (LECA)
> > Freshwater and OCeanic science Unit of reSearch (FOCUS)
> > Université de Liège (Belgique)
> > &
> > Genetics and Ecology of Amphibians Research Group
> > Center for Evolutionary Ecology and Ethical Conservation
> > Laurentian University (Canada)
> >
> > Tel: (+33) (0)6.59.32.29.15 <+32%204%20366%2050%2077>
> > E-mail: leafieschimeric using gmail.com
> >
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> >
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> >
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