different aic and LL in glmer(lme4) and glimmix(SAS)?

Hello All,

I have read several posts related to this previously, but haven't 
found any resolution yet. When running the same GLMM in glmer and in 
SAS PROC GLIMMIX, both programs return comparable parameter estimates, 
but wildly different likelihoods and AIC values.

In SAS I specify use of the Laplace approximation. In R, I believe 
this is the default (no?).

What's the difference, and [how] can I reproduce the SAS -2ll in glmer?

Thanks,
Jeff

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R_GLMM = glmer(cbind(SdlFinal, SdlMax-SdlFinal) ~ 
lnsdlmaxd*lnadultssdld +

?(1|ID),data=sdlPCAdat,family="binomial")

R_GLMM

Generalized linear mixed model fit by the Laplace approximation
Formula: cbind(SdlFinal, SdlMax - SdlFinal) ~ lnsdlmaxd * lnadultssdld 
+
(1 | ID)
? Data: sdlPCAdat
?AIC ?BIC logLik deviance
?1150 1165 ? -570 ? ? 1140 ? ? ? ?<------------------ this line!!
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?ID ? ? (Intercept) 1.2491 ? 1.1176
Number of obs: 144, groups: ID, 48

Fixed effects:
? ? ? ? ? ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|)
(Intercept) ? ? ? ? ? ? 4.56964 ? ?0.43148 ?10.591 ?< 2e-16 *** 
lnsdlmaxd ? ? ? ? ? ? ?-0.65936 ? ?0.05686 -11.595 ?< 2e-16 *** 
lnadultssdld ? ? ? ? ? -0.64534 ? ?0.15861 ?-4.069 4.73e-05 *** 
lnsdlmaxd:lnadultssdld ?0.07393 ? ?0.02166 ? 3.414 ?0.00064 ***
---
Signif. codes: ?0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
? ? ? ? ? ?(Intr) lnsdlm lndlts
lnsdlmaxd ? -0.923
lnadltssdld -0.461 ?0.479
lnsdlmxd:ln ?0.482 -0.508 -0.994


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title 'SAS GLMM';
proc glimmix data=sdlPCAdat ic=pq noitprint method=laplace; class site 
id; model sdlfinal/sdlmax = lnsdlmaxd|lnadultssdld/ solution 
dist=binomial; random ID /; covtest glm/wald; run;

//////////////////////////////////////////////////////////////////////

? ? ? ? ? ? ? ? ? ? ?SAS GLMM ? ? ?19:36 Wednesday, June 30, 2010 88

? ? ? ? ? ? ? ? ? The GLIMMIX Procedure

? ? ? ? ? ?Data Set ? ? ? ? ? WORK.SDLPCADAT
? ? ? ? ? ?Response Variable (Events) ?SdlFinal
? ? ? ? ? ?Response Variable (Trials) ?SdlMax
? ? ? ? ? ?Response Distribution ? ? Binomial
? ? ? ? ? ?Link Function ? ? ? ? Logit
? ? ? ? ? ?Variance Function ? ? ? Default
? ? ? ? ? ?Variance Matrix ? ? ? ?Not blocked
? ? ? ? ? ?Estimation Technique ? ? Maximum Likelihood
? ? ? ? ? ?Likelihood Approximation ? Laplace
? ? ? ? ? ?Degrees of Freedom Method ? Containment



? ? ? ? ? ? ? ? ?Optimization Information

? ? ? ? ? ? Optimization Technique ? ?Dual Quasi-Newton
? ? ? ? ? ? Parameters in Optimization ?5
? ? ? ? ? ? Lower Boundaries ? ? ? 1
? ? ? ? ? ? Upper Boundaries ? ? ? 0
? ? ? ? ? ? Fixed Effects ? ? ? ? Not Profiled
? ? ? ? ? ? Starting From ? ? ? ? GLM estimates

? ? ? ? ? ? Convergence criterion (GCONV=1E-8) satisfied.

? ? ? ? ? ? ? ? ? ? Fit Statistics

? ? ? ? ? ? ? -2 Log Likelihood ? ? ? ?1653.90 ?<------------------ 
this line!!
? ? ? ? ? ? ? AIC (smaller is better) ?1663.90
? ? ? ? ? ? ? AICC (smaller is better) 1664.33
? ? ? ? ? ? ? BIC (smaller is better) ?1673.25
? ? ? ? ? ? ? CAIC (smaller is better) 1678.25
? ? ? ? ? ? ? HQIC (smaller is better) 1667.43


? ? ? ? ? ? ?Fit Statistics for Conditional Distribution

? ? ? ? ? ? ?-2 log L(SdlFinal | r. effects) ? 1436.44
? ? ? ? ? ? ?Pearson Chi-Square ? ? ? ? ?908.07
? ? ? ? ? ? ?Pearson Chi-Square / DF ? ? ? ?6.31


? ? ? ? ? ? ? ? Covariance Parameter Estimates

? ? ? ? ? ?Cov ? ? ? ? Standard ? ? Z
? ? ? ? ? ?Parm ?Estimate ? ?Error ? Value ? Pr > Z

? ? ? ? ? ?ID ? ?1.2491 ? 0.2746 ? 4.55 ? <.0001


? ? ? ? ? ? ? ? Solutions for Fixed Effects

? ? ? ? ? ? ? ? ? ? ? ? ? ? ?Standard
? ? Effect ? ? ? ? Estimate ? ?Error ? ?DF ?t Value ?Pr > |t|

? ? Intercept ? ? ? ? ? 4.5696 ? 0.4333 ? ?47 ? ?10.55 ?<.0001
? ? lnsdlmaxd ? ? ? ? ?-0.6594 ? 0.05717 ? 93 ? -11.53 ?<.0001
? ? lnadultssdld ? ? ? -0.6453 ? 0.1593 ? ?93 ? -4.05 ? 0.0001
? ? lnsdlmaxd*lnadultssd0.07394 ?0.02174 ? 93 ? ?3.40 ? 0.0010