[R-sig-ME] lmer vs SAS results
bbolker at gmail.com
Wed Nov 24 18:11:28 CET 2010
I got a little carried away: see
<http://www.math.mcmaster.ca/~bolker/misc/preyswim.pdf> for details.
* 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
* 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
* 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:
>> 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
>> 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.
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