[R-sig-ME] Fwd: lme4 Question
Ben Bolker
bbolker at gmail.com
Wed Dec 3 02:55:29 CET 2014
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[cc'ing to r-sig-mixed-models. Sorry about previous accidental e-mail.]
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Subject: lme4 Question
Resent-Date: Tue, 02 Dec 2014 19:09:03 -0500
Date: Tue, 2 Dec 2014 17:08:54 -0700
To: bolker at mcmaster.ca
[snip]
I have ran a bunch of analyses a while back in version 1.0-6, and have
since updated to version 1.1-7. I have gone back to the initial analyses
since I updated the package, and the results have changed. Everything is
mostly the same except for the standard errors and p-values. What was
significant is not anymore in several cases, even though the code and data
are the exact same. See output below:
*Output (lme4_1.0-6) *
[image: Inline image 1]
*Output (lme4_1.1-7) Same output in versions 1.1-6 / 1.1-5*
[image: Inline image 2]
BMB> Summary from image:
> standard errors of first two estimates change a fair amount;
> Z-statistics shrink from {2.6,-2.1,2.9,-1.2,0.9 } to
> {1.3,-1.8,2.5,-1.2,0.88}
[for future reference, it works better on the mailing list if you cut
and paste text]
This statement is the only real change associated with SE that I found
between the versions that I have been using:
*From the news for version 1.1-4*: Standard errors of fixed effects
are now computed from the approximate Hessian by default (see the
use.hessian argument in vcov.merMod); this gives better (correct)
answers when the estimates of the random- and fixed-effect parameters
are correlated (Github #47)
I dug into the subject and it appears that this is the cause of my
inflated SE and p-values. I tried: summary(a1fit1, use.hessian=FALSE),
and as you suggested in the pdf manual, this returned my results from
before.
So, this is suggesting that my previous results are incorrect and that
I should use the estimates from the newer version (which means a full
start over)? Or is there justification for using my previous results
and using the hessian = FALSE command? I.e., how do I know if my fixed
and random effect parameters are correlated?
BMB> Sadly, I believe that the new results are correct. I can't
see any particular justification for using the old results. However,
I would certainly recommend that you try computing confidence intervals
by profile likelihood -- that will be more accurate than the Wald
confidence intervals given by summary() in any case (and independent
of the decision about how to compute the variance-covariance matrix
of the parameters).
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