[R-sig-ME] lmer vs SAS results

[David Duffy]

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.

-- 
| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v