On Wed, 18 Oct 2006, Douglas Bates wrote:
On 10/18/06, Torsten Hothorn <[EMAIL PROTECTED]> wrote:
On Wed, 18 Oct 2006, SCHMERA Dénes wrote:
Dear All,
I would like to run a generalized linear mixed model with the software R (one
categorical predictor, one random factor, the distribution of the dependent
variable is binomial, and the link is logit). Thereafter, I would like to
perform multiple comparisons (post hoc test) among the groups of the
categorical predictor.
Is it possible with the software R?
yes and no. There is the `multcomp' package available but the CRAN version
is limited to linear models (more or less). However, I'm currently working
on a re-implementation which has solutions for your problem and much
more :-)
For the time being, the development version is available from
http://www.imbe.med.uni-erlangen.de/~hothorn/multcomp2
The basic steps would be as follows:
### fit logistic regression model
my.glm <- glm(response ~ myfactor, data = <mydata>, family = binomial())
### set up linear hypothesis for coef(my.glm) corresponding to all-pair
### comparisons
library("multcomp2")
gh <- glht(my.glm, linfct = mcp(myfactor = "Tukey"))
### simultaneous confidence intervals with plots
confint(gh)
plot(confint(gh))
The original question was about generalized linear mixed models which
is going to be even more difficult. :-(
oh, oups, didn't read the question carefully enough. The framework is
general enough that one can feed in `lmer' objects into `glht'. It is
assumed that the fixed effects coefficients are asymptotically joint
normal with covariance as reported by `vcov()' and confidence intervals
and (adjusted) p-values are valid only asymptotically.
However, if this is really appropriate is a question I'm not able to
answer :-(
Torsten
Are traditional methods of multple comparisons (for instance Tukey test)
adequate for this purpose or not? Does Tukey test consider the binomial
dsitribution of the dependent variable and the logit link?
I am waiting for your answer,
Denes Schmera
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and provide commented, minimal, self-contained, reproducible code.
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