[R-sig-ME] lmer vs SAS results

[Ben Bolker]

  I got a little carried away: see
<http://www.math.mcmaster.ca/~bolker/misc/preyswim.pdf> for details.

  My conclusions:

 * lme and lme4a agree with SAS (and not lme4) in estimating the MLE of
among-trial variance as >0.  There are a variety of differences between
lme4 and lme4a, I don't really know why lme4 performs suboptimally in
this case.

 * lme4a on R-forge (r1088) does not build on my system [gives the
'drtrs' symbol missing error I posted yesterday]; if I revert to r1080 I
can get it built.  (Doug, Martin?)  I was using a slightly older version
here.

 * If you're really trying to test a hypothesis here (rather than find
the best predictive model), and if you're obeying the magic "p=0.05"
rule, you may be out of luck; the p-value for the LRT between the model
with (swim+light) and (swim) alone is 0.0537.  This should (?) be a
fairly reliable number because the number of groups and data points is
fairly large (lme gives a similar p-value for the F test, which it
claims has 80 denominator df).  Looking at the pictures, I don't see
much of an effect of light jumping out at me except (maybe) at the
lowest light level in the "preyswim=N" group. Maybe the trial effect is
blurring the picture, or ???

On 10-11-23 07:16 PM, David Duffy wrote:
> On Tue, 23 Nov 2010, Beth Holbrook wrote:
> 
> lme4:
>>       Df    AIC    BIC   logLik  Chisq Chi Df Pr(>Chisq)
>> model3  4 214.21 229.98 -103.103
>> model2  9 215.26 250.74  -98.628 8.9494      5     0.1111
>> model1 14 219.15 274.35  -95.575 6.1062      5     0.2960
>>
> SAS:
>>               AIC    BIC    -2 Log Likelihood
>> model1   216.7  251.1   188.7
>> model2   213.3  235.4   195.3
>> model3   214.2  224.0   206.2
>>
> 
> Well, SAS agrees with lme here (with method="ML"):
> 
> Model df      AIC      BIC     logLik   Test  L.Ratio p-value
> m1    14 216.7262 271.9254  -94.36309 1 vs 2  6.59408  0.2526
> m2     9 213.3203 248.8055  -97.66013 2 vs 3 10.88519  0.0537
> m3     4 214.2055 229.9767 -103.10273
> 
> Directly maximizing the likelihood (using AS319), I get the
> m2 v. m3 LRTS to be 10.8852.
> 
> I haven't evaluated likelihood at the lmer and SAS solutions yet, but
> obviously the likelihood surface will be fairly flat.
> 
> Cheers, David Duffy.
>