[R] GLMMs fitted with lmer (R) & glimmix (SAS)

[andrea previtali]

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying method="PQL" so as to make the estimation method comparable between lmer and GLIMMIX.
It is difficult to compare the outputs for the interaction terms because SAS gives the estimates and significance value for each of the categories, whereas R gives a single estimate for the interaction term. But, from the main effects it is possible to see very similar estimates obtained with either program.
I am very interested in the interaction term SEX*ELI, and this term comes up as significant in SAS and nonsignificant in R. Why could this be? It is very worrisome to think of reporting a significant result that is not validated when doing a similar analysis using a different program!

Can somebody help me interpret these differences?
Bellow is a summary of the outputs obtained with R and SAS.
Thanks,
Andrea Previtali
Post-doc fellow
Dept. of Biology,
Univ. of Utah.

lmer output:

Generalized linear mixed model fit using PQL 

Formula: SURV ~ SEX * ELI + DW * DIST + SEAS + DEN + WT + (1 | SITE) 

Family: binomial(logit link)

AIC  BIC logLik deviance

1539 1606 -758.7     1517

Random effects:

Groups Name        Variance Std.Dev.

SITE   (Intercept) 0.27816  0.52741 

number of obs: 3104, groups: SITE, 19


Estimated scale (compare to  1 )  0.9458749 


Fixed effects:

                  Estimate     Std. Error    z value Pr(>|z|)    

(Intercept) -1.144259   0.458672    -2.495   0.012606 *  

SEX          -0.606026   0.167289    -3.623   0.000292 ***

ELI           -0.190757   0.219599    -0.869    0.385034    

DW           -0.328796   0.175882    -1.869   0.061565 .  

DIST         -0.117745   0.374148    -0.315   0.752989    

SEAS        -0.784971   0.158748    -4.945   7.62e-07 ***

DEN          -0.013381   0.002585    -5.176   2.27e-07 ***

WT             0.007735   0.019115     0.405    0.685732    

SEX:ELI    -0.466425   0.461596    -1.010   0.312274    

DW:DIST  -1.015454   0.404683    -2.509   0.012099 *  

-----------------------------------------------------------------------------------
GLIMMIX output:

Model Information

 Variance Matrix Blocked By    Site

 Estimation Technique:  Residual PL

 Degrees of Freedom Method:  Containment
       

Fit Statistics

-2 Res Log Pseudo-Likelihood:    17868.73
Pseudo-AIC: 17890.73
Pseudo-BIC: 17957.14

Covariance Parameter Estimates
Cov Parm     Subject    Estimate       Std Error
Intercept        Site         0.2975      0.1799

Solutions for Fixed Effects 
Effect           DIST  DW     ELI  SEX    SEAS 		Estimate Std Error  DF  t Value  Pr> |t|
Intercept                                                           -4.6540    0.6878     17    -6.77     F
DIST*DW		       	          3        3077       6.06    0.0004
SEX*ELI              		   3        3077       6.30    0.0003
WT                 		      1        3077       0.16    0.6918
SEAS                  		     1        3077      24.37