[R-lang] What happens when a random effect has a st. dev. of close to zero?
gillespie.maureen at gmail.com
Fri Aug 21 15:41:12 PDT 2009
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!)
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 necessary?
And, more on the conceptual end of things, why would a near-zero st.dev. of
a random effect inflate CIs w/MCMC sampling?
Thanks in advance,
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