This is a summary and extension of the thread
"GLMM (lme4) vs. glmmPQL output"
http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html
In the new revision (#Version: 0.4-7) of lme4 the standard
errors are close to those of the 4 other methods. Thanks to Douglas Bates,
Saikat DebRoy for the re
Prof Brian Ripley wrote:
> Although it has not been stated nor credited, this is very close to an
> example in MASS4 (there seems a difference in coding).
I apologize for the oversight. This is to state that the code starting
> data(bacteria,package="MASS")
> UseMASS<-F# must restart R after ch
Although it has not been stated nor credited, this is very close to an
example in MASS4 (there seems a difference in coding). Both the dataset
and much of the alternative analyses are from the work of my student James
McBroom (and other students have contributed).
MASS4 does contain comparisons
"Dieter Menne" <[EMAIL PROTECTED]> writes:
> I have compared glmmPQL, glmmML, geese and GLMM, results and code see below.
> I am aware that glmmPQL uses another method to handle the problem, and
> geese (geepack) has considerable different assumptions, but the
> results are very similar. On the ot
Goran,
from my reply to a message from Douglas Bates; ">" is quoted from a mail by
DG.
> 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
>
On Fri, Jan 09, 2004 at 12:26:21PM -0600, Douglas Bates wrote:
> 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 corresp
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
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
