I am using lmer in lme4 to run mixed-effects conditional logistic regression
as part of a habitat selection analysis on gopher tortoises. In my study, I
collected data on 18 gopher tortoises and measured habitat variables at each
telemetry location. I then differenced the values of these variables between
used and random points and fit a no-intercept model in lme4 with a random
slope for each individual:

 

lmer(status~-1+Density+(-1+Density|Name),data=SA.data,family='binomial',weig
hts=Weight,start=mod1$coef)

 

I obtained the starting values from a glm model with the same structure
(minus the random effect) and weights are the number of times an individual
was located at a particular location.

However, the main purpose of the analysis was to test for differences in
selection between two groups of tortoises, resident tortoises and
translocated tortoises. However, individual is completely nested within
treatment (10 translocated tortoises and 8 resident tortoises). My attempts
to include treatment as a fixed effect have either caused failed convergence
or abnormally large standard errors. 

Do you have any suggestions which might allow me to test for a treatment
effect? Would it be appropriate to include only a nested random effect
(1+Density|Treatment/Name)? I am also concerned about the potential for
habitat*treatment interactions. 

My data are quite sparse (2-10 observations per individual) and I cannot
include more than one or two independent variables per model in order for
the model to converge. Under these constraints, I am wondering if the best
scenario would be to run separate models for each treatment and compare the
beta estimates and their 95% confidence intervals from the best selected
models for each treatment rather than trying to fit many parameters to a
single model.

 

Thank you very much for your help

 

J. Bauder 

 

 


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