[R-sig-ME] Response variable with zero variance
Joshua Wiley
jwiley.psych at gmail.com
Sun Aug 12 03:38:02 CEST 2012
On Sat, Aug 11, 2012 at 6:31 PM, Ben Bolker <bbolker at gmail.com> wrote:
> Tonya Haff <tonyahaff at ...> writes:
>
>> I used the lmer package, using the following code
>>
>> FL.glmm <- lmer(RESPONSE ~ fEXPN + fPBN + fEXPN*fPBN + (1|fNID), data =
>> FL.1, family = binomial)
>
> By the way, the fixed effect part of the formula can also
> be written as fEXPN*fPBN alone (since A*B is equivalent to A+B+A:B;
> unlike in some other stats packages, * means "crossed" rather
> than "interaction")
>
> It may be worth double-checking this with glmmPQL:
>
> library(MASS)
> glmmPQL(RESPONSE~fEXPN*fPBN,random=~1|fNID,data=FL.1,
> family="binomial")
>
> although if the data are Bernoulli (0/1) then PQL is a little
> bit questionable.
>
>>
>> Here is the summary
>>
>> Generalized linear mixed model fit by the Laplace approximation
>> Formula: RESPONSE ~ fEXPN + fPBN + fEXPN * fPBN + (1 | fNID)
>> Data: FL.1
>> AIC BIC logLik deviance
>> 104.1 163.0 -35.04 70.09
>> Random effects:
>> Groups Name Variance Std.Dev.
>> fNID (Intercept) 0.047168 0.21718
>> Number of obs: 236, groups: fNID, 15
>>
>> Fixed effects:
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) -0.4102 0.5304 -0.774 0.439
>> fEXPN2 1.4329 0.7880 1.818 0.069 .
>> fEXPN3 21.9715 12840.4523 0.002 0.999
>> fEXPN4 21.9715 12409.4403 0.002 0.999
>> fPBN2 21.9715 12409.4408 0.002 0.999
>> fPBN3 0.5453 0.7390 0.738 0.461
>> fPBN4 -1.4788 0.9286 -1.593 0.111
>> fEXPN2:fPBN2 -1.4171 17619.4645 -1e-04 1.000
>> fEXPN3:fPBN2 -21.9715 21994.2881 -0.001 0.999
>> fEXPN4:fPBN2 -21.9715 21493.7828 -0.001 0.999
>> fEXPN2:fPBN3 20.0155 12547.8259 0.002 0.999
>> fEXPN3:fPBN3 -0.5390 18188.0409 0.000 1.000
>> fEXPN4:fPBN3 -0.5390 17577.5297 0.000 1.000
>> fEXPN2:fPBN4 -21.1113 12447.6988 -0.002 0.999
>> fEXPN3:fPBN4 -41.6658 18265.7721 -0.002 0.998
>> fEXPN4:fPBN4 -41.6660 17647.6445 -0.002 0.998
>
> This looks like a Hauck-Donner effect to me (you can look it
> up -- it occurs in GLM(M)s when there are strong binomial
> effects). I'm a little worried about the exact equivalence
> of some of the parameter estimates though, and apparent
> overfitting ... if these are Bernoulli data, how many
> positive responses are there overall? Have you tried
> nAGQ=8 ?
>
>> So here I had no F statistic at all, and the results of the fixed effects
>> don't make sense, when compared against the actual data (where the pattern
>> between PBN (playback type) and EXPN (experiment) are quite stark.
>>
>> Because this didn't make sense to me, and because I am not a whiz at R, I
>> ran the same model in SPSS 20.0, and that is where I got the huge F
>> statistic. Here is that model summary:
>>
>> F = 1, 229, 656.019, df1 = 15, df2=220, P<0.0001
>> EXPN F = 83.83, df1=3, df2=220, P<0.0001
>> PBN F=2,2280.08, df1=3, df2=220, P<0.0001
>> EXPNxPNB F=82.96, df1=9, df2=220, P<0.0001
>
> But: what did you do in SPSS, which doesn't fit GLMMs (as
> far as I know)?
SPSS does fit GLMMs (using PQL, I believe). It is not a pleasant
interface, but here are examples:
http://www.ats.ucla.edu/stat/spss/code/hdp.htm
which are meant to analyze this (simulated) dataset:
http://www.ats.ucla.edu/stat/r/pages/mesimulation.htm
although I had to give up the right censored mixed effects poisson
(just ran as poisson in SPSS) and the mixed effects beta (ran as
binomial in SPSS).
>
>> >
>> > Ideally, I would like to run a GLMM on my data, which are are binomial (so
>> > I'm trying to run a model with a binomial distribution and a logit link).
>> > I have two fixed effects, fledgling age and playback type. I have
>> > fledging ID as a random effect. The problem I'm running into is zero
>> > variance in my response variable - ie fledglings always respond the same
>> > way to some of the playback types. I can run a model and it converges, but
>> > it spits out an F value of over a million. So I'm wondering (1) is it
>> > possible to use a GLMM with a zero variance response; and (2) if so, which
>> > R package is the most appropriate?
>> >
>> >
>
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--
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/
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