Dear list,

I have two problems when I try to use mixed models. First as far as I know,
there are two main implementations of mixed models: lme4 and MCMCglmm.

I try to model a binary response variable over a small period of time. The
problem is that for some lines, the response is missing. In this mailing
list archive, I do not find response to this question.

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/002940.htmlproposes
the MCMCglmm but I check the package and missing data are not
handled

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q3/002579.html, no
solution

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001794.htmlproposes
the EM algorithm to solve the problem

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2008q3/001188.html says
missing data in covariates can be handled directly by R or SAS.

As I'm a beginner in the use of mixed models, I spent a lot of times reading
on this topic. And in the book of Edward Frees 'longitudinal and panel
data', estimation seems to be available for missing data? And there is an
article promoting the use of mixed model for that feature:
'A Comparison of the General Linear Mixed Model and Repeated Measures ANOVA
Using a Dataset with Multiple Missing Data Points'

So I do not understand why we can't have missing data?

Secondly, the presentation of D. Bates done at Max Planck Institute in 2009
states that p-values are not available for mixed models because the
distribution of parameter estimators are not known.

My question is how can we know that an explanatory variable is significant?
is the only tool to fit another model without the variable and to use the
anova function?

Thanks in advance

Christophe

-- 
Christophe DUTANG
Ph. D. student at ISFA

	[[alternative HTML version deleted]]


