[R-lang] What happens when a random effect has a st. dev. of close to zero?
rlevy at ling.ucsd.edu
Sat Aug 22 07:32:59 PDT 2009
On Aug 21, 2009, at 3:41 PM, Maureen Gillespie wrote:
> Hi everyone,
> I have been using weighted empircal logit linear regression (Barr,
> 2008) to analyze data from a number of agreement error production
> experiments. (Just as a side note, I have run into lots of problems
> trying to use logit mixed models for this data as errors are
> extremely rare: certain conditions produce essentially no errors and
> all other conditions rarely have higher than 15% error rates. If
> anyone has a better solution than the emp.logit please let me know!)
Yes, I have had similar difficulties with logit models where at least
one condition is error-free. One thing you may be prone to running
into in these cases is the unreliability of the Wald z-statistic.
Search for "standard error is inflated" on this page:
> That being said, I am running what is essentially a meta-analysis.
> I have data from 5 experiments and 104 different items (some of
> which appear in multiple experiments, some only appear in a single
> experiment). My model has two continuous predictors and two random
> effects (experiment and item).
> lmer(emp.logit ~ IV1 + IV2 + (1|item) + (1|exp), data, weights)
> When I run the model, my estimates, standard errors, and t-values
> all appear reasonable (i.e., comparable to other single random
> effect models I have run using this technique on similar data).
> There is no colinearity or anything else to suggest that something
> is wrong. But when I use pvals.fnc() to compute CIs and p values
> for the estimates, I find that the experiment random effect has a
> std. dev. of 0.0000 (5.0e-11 to be exact), and this seems to inflate
> the CI of the intercept estimate (t = 17, but it's only marginal
> significant w/ pvals from MCMC). If I run the same model excluding
> the experiment random effect, estimates do not change and the CIs
> and p values for the intercept appear normal. Strangely (or maybe
> not) the two models have the exact same log likelihoods.
> Is this just an extreme example of a random effect not being
> And, more on the conceptual end of things, why would a near-zero
> st.dev. of a random effect inflate CIs w/MCMC sampling?
I'm not sure what you mean by inflating the CI -- do you mean making
the CI on the fixed-effect intercept larger than it is in a model
without the random effect of experiment?
With such a small random effect of experiment, the model probably *is*
telling you that you don't need it. Try comparing your model's
likelihood with the likelihood of a model that doesn't have the random
effect of experiment -- the likelihoods should be very similar.
(Technically it's best to use restricted maximum likelihood
(REML=TRUE) when doing this, but that is the default so it looks like
you're doing that already.
> Thanks in advance,
> Maureen Gillespie
> Northeastern University
> R-lang mailing list
> R-lang at ling.ucsd.edu
Roger Levy Email: rlevy at ling.ucsd.edu
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