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

[Thierry Onkelinx]

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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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/
> ##################################################
>
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>

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