[R-sig-ME] mixed model with recapture data

[Leandro Rabello Monteiro]

Dear Thierry
Thanks a lot for the advice. Our bats (Carollia perspicillata) do not
hibernate, but their body condition also presents a seasonal pattern of
change, with a decrease after the breeding season (middle of the year, dry
season here), particularly strong for females. I implemented the month and
year factors as you suggested, but also added an interaction with sex. The
dimorphic seasonality in condition helped explain the main result, that
females are more sensitive than males to lesions caused by arm bands.
Best regards,
Leandro

##################################################
Leandro R. Monteiro
Laboratorio de Ciencias Ambientais
Universidade Estadual do Norte Fluminense
E-mail: lrmont using uenf.br
CV Lattes: http://lattes.cnpq.br/4987216474124557
WS: https://sites.google.com/uenf.br/ecol-evolucao-de-mamiferos/
<http://sites.google.com/site/morphogroup/>
English WS: https://sites.google.com/uenf.br/mammalecologyandevolution/
##################################################


Em sex., 25 de set. de 2020 às 04:18, Thierry Onkelinx <
thierry.onkelinx using inbo.be> escreveu:

> Dear Leandro,
>
> You could consider splitting the time effect into a year effect and a
> month effect. This will assume that every year has the same seasonal
> pattern. Add year as a fixed effect factor if your data spans only a few
> years.
>
> lm.smi <- lmer(SMI ~ Sex * MarkR + Year + (1 | ID) + (1 | Month), data =
> smi)
>
> The bats in our region are hibernating. Their body condition peaks in the
> early autumn and is low in early spring. You can model such a pattern with
> e.g. a sine wave as fixed effect and a random effect to model the
> deviations from the sine wave.
> Month_rad <- 2 * pi * Month / 12
> sin(Month_rad) + cos(Month_rad) + (1 | Month)
>
> Notethataddingspacestotextmakesitmuchmorereadable.Thesamegoesforcode.
>
> 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
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
>
> ///////////////////////////////////////////////////////////////////////////////////////////
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> 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
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> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> ///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op do 24 sep. 2020 om 22:47 schreef Leandro Rabello Monteiro <
> lrmont using uenf.br>:
>
>> Dear All
>>  I am trying to evaluate the body condition (SMI) of bats in a
>> mark-recapture study, in response to lesions caused by arm bands.
>> Because recapture is a matter of chance, the design is highly
>> unbalanced. Most individuals were recaptured twice, but there can be
>> up to 18 recaptures in a period of 4 years.
>>
>> The data set is formatted in a way that each line is one individual at
>> a point in time. The head() of the data frame looks like this
>>
>>   ID Sex      SMI MarkR YearMonth
>> 1  1   M 15.10700    L0   2013-04
>> 2  1   M 14.52348    L0   2013-06
>> 3  1   M 15.51033    L0   2013-07
>> 4  1   M 15.51033    L0   2013-09
>> 5  1   M 15.26151    L0   2013-11
>> 6  1   M 15.33953    L0   2014-08
>>
>> ID is a factor to identify individuals, MarkR (response to banding) is
>> a factor with levels (NR =  no ring, the first capture, L0 = ringed,
>> no lesion, L1 = lesion type 1, L2 = lesion type 2). A single
>> individual can change its level in MarkR, so it is a within-subject
>> fixed factor. Some individuals will develop lesions and some will not.
>> The question of interest is whether banding itself or lesions caused
>> by banding can be associated with lower SMI, so the only comparisons
>> of interest are the levels L0-2 against the "control" NR.
>>
>>  Lesions, particularly L2 are rare, occurring in ~3% of observations
>> (out of 2400), again with a high unbalance among levels. There is some
>> seasonality in body condition, but I am not particularly interested in
>> this aspect right now, but I am not sure about the best way to include
>> the temporal factor YearMonth it in the model.
>>
>> I have tried the following, using individuals and YearMonth as random
>> effects.
>> lm.smi<-lmer(SMI~Sex*MarkR+(1|ID)+(1|YearMonth),data=smi)
>>
>> I would appreciate some guidance as to whether I might be missing
>> something relevant, particularly due to the highly unbalanced design.
>> I have searched a lot but have not managed to find similar examples in
>> the literature or the web. Thanks a lot for your time.
>>
>>
>> ##################################################
>> Leandro R. Monteiro
>> Laboratorio de Ciencias Ambientais
>> Universidade Estadual do Norte Fluminense
>> E-mail: lrmont using uenf.br
>> CV Lattes: http://lattes.cnpq.br/4987216474124557
>> WS: https://sites.google.com/uenf.br/ecol-evolucao-de-mamiferos/
>> English WS: https://sites.google.com/uenf.br/mammalecologyandevolution/
>> ##################################################
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>

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