[R-sig-ME] extractor function for coefficient table fromsummary.mer ?

[Douglas Bates]

On Wed, Jun 17, 2009 at 4:59 PM, Ben Bolker<bolker at ufl.edu> wrote:
> David Duffy wrote:
>> On Wed, 17 Jun 2009, Douglas Bates wrote:
>>
>>> On Tue, Jun 2, 2009 at 10:05 PM, David Duffy<David.Duffy at qimr.edu.au> wrote:
>>>> On Tue, 2 Jun 2009, Ben Bolker wrote:
>>>>>  Request for comment: would it be reasonable to have the
>>>>> "coef" method for "summary.mer" objects return the table
>>>>> of parameter values, standard errors etc.?
>>>> Yes please, oh and a profile likelihood based confint.lmer() too,
>>>> thanks ;).
>>> I have been thinking about this recently and I have a way of
>>> constructing a profile likelihood for the variance component
>>> parameters.  Are those the parameters that are of interest or are you
>>> more interested in the fixed-effects parameters?
>>>
>>
>> Yes, the variance components are of direct interest.
>>
>>> Yet somehow the variability in estimates of variance components in
>>> much more complicated models can be expressed by quoting a standard
>>> error.
>>>
>>
>> Yes, we usually try and produce appropriate confidence intervals and/or
>> interpretable likelihood based test statistics.  The latter, of course,
>> are tricky mixtures for multivariate hypotheses -- a typical one for us is
>> a variance components linkage analysis test that the common component due
>> a particular genome region is zero for three measures (repeated at 3
>> occasions, but with differing contributions by occasion).  People still
>> want a P-value, so they can carry out adjustment for genome-wide testing
>> (linkage is supposed to be roughly equivalent to 50-60 tests for a human
>> length genome, but the genome-wide corrected 5% P-value is usually quoted
>> as 2e-5).
>>
>> Cheers, David Duffy.
>>
>
>  Fixed effect profiles are interesting too (to me) ... I have written
> some of my own code to do this (happy to make it available), but it's
> not very general/robust at the moment.

I think there is a general way of creating the profiles including with
respect to the fixed-effects parameters but, as always, the devil is
in the details.  I have already tripped up on the simplest case of
models like

lmer(Yield ~ 1 + (1|Batch), Dyestuff)

When you condition on the value of the one and only fixed-effects
parameter the code for the penalized least squares solution becomes
confused because the reduced model matrix for the fixed effects has
zero columns.