GLMM (lme4) vs. glmmPQL output

I believe the distinction is explained in the lme4 documentation but,
in any case, the standard errors and the approximate log-likelihood
for glmmPQL are from the lme model that is the last step in the
optimization.  The corresponding quantities from GLMM are from another
approximation that should be more reliable.

"Dieter Menne" <dieter.menne at menne-biomed.de> writes:

Dear List,

As I understand, GLMM (in experimental lme4) and glmmPQL (MASS) do
similar things using somewhat different methods. Trying both,
I get the same coefficients, but markedly different std. errors and
p-values.
Any help in understanding the models tested by both procedures?

Dieter Menne


UseMASS<-T # must restart R after changing because of nlme/lme4 clash
if (UseMASS){
  library(MASS)
  summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                  family = binomial, data = bacteria))
} else
{
  library(lme4)
  summary(GLMM(y ~ trt + I(week > 2), random = ~ 1 | ID,
                  family = binomial, data = bacteria,method="PQL"))
}

(MASS output)
Fixed effects: y ~ trt + I(week > 2)
                    Value Std.Error  DF   t-value p-value
(Intercept)      3.412012 0.5185028 169  6.580509  0.0000
trtdrug         -1.247355 0.6440627  47 -1.936698  0.0588
trtdrug+        -0.754327 0.6453971  47 -1.168780  0.2484
I(week > 2)TRUE -1.607256 0.3583378 169 -4.485310  0.0000

(lme4 output)
Fixed effects: y ~ trt + I(week > 2)
                 Estimate Std. Error  DF z value Pr(>|z|)
(Intercept)       3.41202    3.93293 169  0.8676   0.3856
trtdrug          -1.24736    1.52156  47 -0.8198   0.4123
trtdrug+         -0.75433    1.21963  47 -0.6185   0.5363
I(week > 2)TRUE  -1.60726    2.19660 169 -0.7317   0.4644

-- 
Douglas Bates                            bates at stat.wisc.edu
Statistics Department                    608/262-2598
University of Wisconsin - Madison        http://www.stat.wisc.edu/~bates/