[R-sig-ME] Lmer and variance-covariance matrix
bates at stat.wisc.edu
Fri Mar 11 21:45:27 CET 2011
On Fri, Mar 11, 2011 at 2:37 PM, Rolf Turner <r.turner at auckland.ac.nz> wrote:
> On 12/03/11 02:56, Jarrod Hadfield wrote:
>> In addition, each trait is only measured once for each id (correct?)
>> which means that the likelihood could not be optimised even if the
>> data-set was massive. If you could fix the residual variance to some
>> value (preferably zero) then the problem has a unique solution given
>> enough data, but I'm not sure this can be done in lmer?.
> I think that it ***CANNOT*** be done. I once asked Doug about
> the possibility of this, and he ignored me. As people so often
> do. :-) Especially when I ask silly questions .....
Did Doug really ignore you or did he say that the methods in lmer are
based on determining the solution to a penalized linear least squares
problem so they can't be applied to a model that has zero residual
variance. Also the basic parameterization for the variance-covariance
matrix of the random effects is in terms of the relative standard
deviation (\sigma_1/\sigma) which is problematic when \sigma is zero.
(My apologies if I did ignore you, Rolf. I get a lot of email and
sometimes such requests slip down the stack and then get lost. I'm
very good at procrastinating about the answers to such questions.)
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