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GLMM
2 messages · clemens.tilke@web.de, Brian Ripley
On Mon, 1 Nov 2004 clemens.tilke at web.de wrote:
I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program).
You haven't really attributed the functions you use to particular packages. If this is glmm() from Jim Lindsey's packages then it was our experience around the time that MASS was written that is often discordant with other implementations/approaches. As a result we have not used it more recently.
I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the intercept e.g. I have a standard error of 0.4354 from glmm and 0.5338 from glmmML. Any idea about this problem??
My main reaction is that the intercept is not an interesting parameter in that problem and that small differences in standard error are not of any practical consequence. So what exactly is `this problem'?
Thanks in advance
Clemens Tilke
library (MASS)
data (bacteria)
contrasts (bacteria$trt) <- structure (contr.sdif (3),
dimnames = list (NULL, c ("drug", "enc")))
bacteria.pql <- glmmPQL (y ~ trt + I(week > 2), random = ~ 1 |
ID, family = binomial, data = bacteria)
summary (bacteria.pql)
library (repeated)
y1 <- 1 * (bacteria$y == "y")
bacteria1 <- data.frame (bacteria, y1 = y1)
bacteria.glmm <- glmm (y1 ~ trt + I(week > 2), nest = ID, data
= bacteria1, family = binomial)
summary (bacteria.glmm)
library (glmmML)
bacteria.glmmML <- glmmML (y1 ~ trt + I(week > 2), cluster =
bacteria1$ID, data = bacteria1, family = binomial)
summary (bacteria.glmmML)
bacteria.glmmML
Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595