[R] GLMM (lme4) vs. glmmPQL output

[Dieter Menne]

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