Logistic regression with repeated measures ?

Hi Peter,

Does it have the ability to fit random effects? Or some other way to
address the pseudoreplication expected in RSF studies using GPS fix data
with little time between fixes ? (Just had a quick look at the rspf
package and I couldn't see any)



Chris Howden B.Sc. (Hons) GStat.
Founding Partner
Evidence Based Strategic Development, IP Commercialisation and Innovation,
Data Analysis, Modelling and Training
(mobile) 0410 689 945
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chris at trickysolutions.com.au




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-----Original Message-----
From: r-sig-ecology-bounces at r-project.org
[mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of Peter Solymos
Sent: Thursday, 28 November 2013 10:33 AM
To: marieline gentes
Cc: r-sig-ecology at r-project.org
Subject: Re: [R-sig-eco] Logistic regression with repeated measures ?

Marie,

Your problem and data seems to me a resource selection problem with
matched use-availability design. Estimating procedure for that design is
discussed in Lele and Keim (2006, Ecology 87:3021--3028) and implemented
in the ResourceSelection package: rspf function, see description of
argument 'm'
for specifying matched points for individual birds. The output is a model
for probability of selection given the distribution of environmental
covariates available for these specific individuals.

Cheers,

Peter

--
PC)ter SC3lymos, Dept Biol Sci, Univ Alberta, T6G 2E9, Canada AB
solymos at ualberta.ca, Ph 780.492.8534, http://psolymos.github.com Alberta
Biodiversity Monitoring Institute, http://www.abmi.ca Boreal Avian
Modelling Project, http://www.borealbirds.ca


On Wed, Nov 27, 2013 at 2:29 PM, marieline gentes

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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