Significance and lmer

Adam D. I. Kramer <adik at ...> writes:

Dear colleagues,

Please consider this series of commands:

a <- lmer(log(stddiff+.1539) ~ pred + m*v + option + (option|studyID),
data=r1, subset=option>1, REML=FALSE)

b <- update(a, . ~ . - pred)

anova(a,b)

...am I mistaken in thinking that the latter command will produce a test of
whether "pred" is a significant predictor of log(stddiff+.1539)? I am
concerned because of the results:

[snip]

...a significant result completely unrelated to the t-value. My
interpretation of this would be that we have no good evidence that the
estimate for 'pred' is nonzero, but including pred in the model improves
prediction.

It is possible for Wald tests (as provided by summary()) to 
disagree radically with likelihood ratio tests (look up "Hauck-Donner
effects", but my guess is that's not what's going
on here (it definitely can apply in binomial models, don't think
it should apply to LMMs but ?).

  I have seen some wonky stuff happen with update() [sorry, can't
provide any reproducible details], I would definitely try fitting
b by spelling out the full model rather than using update() and
see if that makes a difference.

  Other than that, nothing springs to mind.

  (Where does the log(x+0.1539) transformation come from???)