The analysis I am running is a mixed-effects conditional logistic
regression. My colleagues and I are using these models to conduct
habitat selection analyses with wildlife telemetry data where each
telemetry point is paired with a measure of availability unique to
that point. We then difference the value of our independent variable
at the telemetry point (used) with the measure of availability for
that variable and fit a no-intercept logistic regression model to
those differences and use individual animal as a random slope effect.
We have used the following model structure:
lmer(1 ~ ?1+diff100+?(-1+diff100+?|Individual),
data=data, family=?binomial?)
Do you *really* have a 1 on the left-hand side of the formula?
It's hard for me to even understand what this mean/what kind of
sensible results this would produce ...
Whenever we have used this specification in the newer versions of lme4
(v 1.0-4 and above) we receive an error. For example, when using lmer
and lme4 v 1.0-4, I receive the following:
[snip lmer example -- no real reason to even try that at this point]
When we try using glmer we receive the following error:
Error in function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L,
control = glmerControl(), :
Response is constant - cannot fit the model
I guess I don't understand your example sufficiently well.
This error should only be triggered when there is only a single
unique value of the response variable, and I can't figure out
why that should happen in your case. Can you provide a reproducible
example? The following trivial example works, but I guess it doesn't
look like your data ...
set.seed(101)
input <- expand.grid(Density=1:20,Name=factor(1:20))
input$status <- rbinom(nrow(input),size=1,prob=0.5)
library(lme4)
mod1 <- glmer(status~-1+Density+(-1+Density|Name),
data=input,family='binomial')
Do you know of any ways to run this type of model structure in the new
versions of lme4 (or any other R package)? We would really appreciate
any insights you could provide.
Can you point me to a reference for this type of model?
If I can convince myself that it ever makes sense to allow a model
with a constant response value, we could allow an option to glmerControl
to override/ignore the test for unique values
Also note that conditional logits are _not_ quite the same thing
as a regular logit model -- see
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q2/016030.htmlhttp://data.princeton.edu/wws509/notes/c6s3.html
There is probably a way to set this up with the machinery of
lme4, but I haven't thought it through -- add it to the ridiculously
long list of things I think are possible but haven't had the time
to work out.
Ben Bolker