Leandro--
2 more things I would think about:
1: Are lesion types ordered with L2 being larger or more serious lesions than L1: L0 < L1 < L2, or are L1 and L2 different lesion types without one being stronger than the other? And, can (do) L1 or L2 individuals sometimes heal or revert to L0 over time?
What are the possible transitions of MarkR for any individual between recaptures? My thinking is that causality could go in either direction: high SMI individual might be less likely to develop lesions, or lesions could lead to reductions in SMI. Therefore, I would run a second analysis asking if SMI at recapture I predicts transitions toward lesions or more sever lesions at recapture i+1. That might have to be restricted to a subset where the 2 recaptures are only 1 or a few months apart, and, since your exchange with Thierry past this draft reply by, you may need to consider seasonality..
2: I suspect that you want to use SMI when MarkR is NR (the first capture) as a "baseline" for that individual. If so, you may want to make sure NR is the first rather than last level of MarkR.
Tom
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From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> On Behalf Of Leandro Rabello Monteiro
Sent: Thursday, September 24, 2020 1:28 PM
To: r-sig-mixed-models at r-project.org
Subject: [EXTERNAL] [R-sig-ME] mixed model with recapture data
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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.
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Leandro R. Monteiro
Laboratorio de Ciencias Ambientais
Universidade Estadual do Norte Fluminense
E-mail: lrmont at 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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