Dear list,
I am a bit new to logistic regressions. I am working with GPS data from
GPS-tracked birds. My objective is to investigate whether various covariates
influence the probabilty of visiting specific habitats. Each bird has visited
many habitats during the course of its GPS tracking.
Here is a small sample of the data:
Bird.ID Year Sex body.index Recapt PrevWeek.Rain AgriYes AgriNo UrbanYes
UrbanNo
CAL 2010 M 21.99155 13-May-10 1.43 0 100 0 100
CAO 2011 F -19.91797 27-Apr-11 4.23 54 46 9 91
CFL 2010 F 25.61063 12-May-10 2.16 31 69 2 98
CFP 2010 M -30.65814 13-May-10 1.43 60 40 0 100
I understand that I have to use logistic regression, with a cbind code, because
my response variable is not binary anymore (the response is a summary of the
success vs failures).
Based on R tutorials, I am thinking about codes that would look like this:
Agri.RainSex = glm(cbind(AgriYes, AgriNo) ~ PrevWeekRain + Sex + Year +
Year*Sex,family=binomial (logit), data=mydata)
However, contrary to the examples I see online, my data are from individual
birds, not from groups of birds. If I had been using the raw binary data, each
bird would have 100 hundred lines (I converted the percentages into
success/failures)(all my % are weighted the same - that is not a problem here).
Am I supposed to take into account some kind of repeated measure in my model ?
Notes:
For people who are thinking about overdispersed data: my data does not seem to
be overdispersed. But I will inspect that after I am confident that my basic
model is ok. So this question is about dealing with repeated measured, not
about adding a random intercept for overdispersion.
For people who are working with habitat selection models: this is not the case
here. We are not working on resource selection. We want to fit a simple
logistic regression on this data as a part of data exploration. This ultimate
goal is to link contaminant burden with the proportion of time spent in a given
habitat.
Thank you for your advice,
Marie
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