lme4 heteroscedasticity???

On Mon, Oct 13, 2014 at 10:24 AM, Paul Johnson <paul.johnson at glasgow.ac.uk>
wrote:
You can model both heteroscedasticity and crossed random effects in nlme.
Crossed random effects can be handled in nlme using pdBlocked and pdIdent.
See p163-6 of Pinheiro, J.C. & Bates, D.M. (2000). Mixed-Effects Models in
S and S-PLUS. Springer, New York.

But it will be a very slow and memory-intensive process unless one of the
grouping factors has very few levels.  The nlme package does not use sparse
matrix methods, nor does it use a profiled objective criterion.

In other words, yes it is possible but no, it is not easy to do so this way.
On 13 Oct 2014, at 16:12, Douglas Bates <bates at stat.wisc.edu> wrote:

It is best to send questions like this to the
R-SIG-Mixed-Models at R-Project.org mailing list, which I am cc:ing on this
reply.  Several of those who read that list can respond more quickly
than I
am able to.

As far as I know there is not yet the capability in lme4 to model
heteroscedasticity in the distribution of the response given the random
effects.

On Mon, Oct 13, 2014 at 6:01 AM, Ko?melj, Katarina <
Katarina.Kosmelj at bf.uni-lj.si> wrote:

Hello,

I am analyzing a mixed model with three crossed factors, two random
(sample, laboratory) and one fixed (method); the response variable is
the
number of somatic cells in milk.  The main question is: is the
precision of
the means of the three method is comparable? Therefore, I would like to
compare a model with different variances for the methods with the model
considering the same variance for the methods.

In nlme, this is feasible, however, two crossed random factors can not
be
tackled, this can be analyzed with lme4.

In nlme, the problem of heteroscedasticity if solved. Is this problem
solved in lme4 yet?

Do you have any suggestion how to deal with this problem?

Regards,

Katarina

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lme4 heteroscedasticity???

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