mixed model with recapture data

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 at 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 at uenf.br

mixed model with recapture data

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