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
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