Hi Jeff,

Can't answer the question, but out of interest, what happens when you
compare nested models in R and SAS, e.g., models with and without
the lnsdlmaxd:lnadultssdld interaction?  Would be interesting to see the
log-likehood ratio (and maybe /change/ in AIC and BIC between the models).

Cheers,

Andy


On Thu, Jul 1, 2010 at 18:03, Jeffrey Evans <Jeffrey.Evans@dartmouth.edu>wrote:

> 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
>
> \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
> > 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
>
>
>  \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
> 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
>
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>

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