Hi Jarrod,

Thanks for the suggestion. But as I wrote in my previous reply, one issue
with the several alternative models that I've found or that have been
suggested to me is that they don't take random effects, and I have to
include these effects due to the nature of my experiment. It would be great
if you knew any alternative methods that could take random effects.

I found an alternative solution that relies on likelihood-ratio test, in
this link: http://www.mail-archive.com/r-lang@ling.ucsd.edu/msg00115.html .
Based on the proposed solution, since compoundType has 3 levels, a new
predictor "compoundType1" will be created by combining the offending level
"green" (the one with no variance) with another level, as shown below:.

e.g. >compound$compoundType1<-ifelse(compound$compoundType == "green,"
"greenANDred", ifelse(
ompound$compoundType == "red," "greenANDred", "blue")).

This will result in two factors: blue, greenANDred

Then, I will run the following model comparison:
***************************************************
> native.lmer = lmer (word1 ~ compoundType + (1 | subject) + (1|word), data
= compound, family = "binomial",REML=F)
> native2.lmer = lmer (word1 ~ compoundType1 + (1 | subject) + (1|word),
data = compound, family = "binomial",REML=F)


> anova(deacc.lmer,deacc.lmer1)

****************************************************

But I don't really know the underlying logic of this method, so I don't know
how to explain the results obtained from this kind of method. What does it
mean if I obtained significant difference between the two models? What does
it mean if no significance was detected?

An explanation of this method would be much appreciated! Thanks!

_____________________________________
Message: 2
Date: Sat, 31 Jul 2010 14:22:19 +0100
From: Jarrod Hadfield <j.hadfield@ed.ac.uk>
To: Ben Bolker <bbolker@gmail.com>
Cc: Timothy_Handley@nps.gov, r-sig-mixed-models@r-project.org
Subject: Re: [R-sig-ME] What to do when a subset of binomial data has
       only    positive outcomes
Message-ID: <20100731142219.dsk04v6o00w8gw8g@www.staffmail.ed.ac.uk>
Content-Type: text/plain;       charset=UTF-8;  DelSp="Yes";
 format="flowed"

You may want to take a look at A WEAKLY INFORMATIVE DEFAULT PRIOR
DISTRIBUTION FOR LOGISTIC AND OTHER REGRESSION MODELS Gelman et. al.
2008 The annals of applied statistics 2 pp1360-1383 which you will
need to implement in BUGS or JAGS. However, an easier route may be to
to use normal priors on the coefficients and use MCMCglmm. In many
cases I think this prior specification has good properties. For example:

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