[R-sig-ME] Low intercept estimate in a binomial glmm

[John Maindonald]

Well, yes, not necessarily of scant consequence when general
optimisation algorithms are used!

Also, note that type III sums of squares are defined with respect to
a specific parameterisation.  Do not use them unless in the rare
event that one can make a good case for a particular choice of 
parameterisation! 

Random effects are defined with respect to a particular 
parameterisation. 

John Maindonald             email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
http://www.maths.anu.edu.au/~johnm

On 06/04/2013, at 11:49 AM, Ben Bolker <bbolker at gmail.com> wrote:

> John Maindonald <john.maindonald at ...> writes:
> 
>> 
>> Surely it is an issue of how you define multi-collinearity.
>> 
>> Centering is a simple re-parameterisation that, like any
>> other  re-parameterisation, makes no difference to the
>> predicted values and their standard errors (well, it will
>> make some small difference to the numerical computational
>> error, but with modern software that should be of scant
>> consequence).  Re-parameterisation may however give
>> parameters that are much more interpretable, with much
>> reduced correlations and standard errors   That is the
>> primary reason, if there is one, for doing it.
>> 
> 
>  ... but unfortunately centering often *can* make a difference
> in GLMM fitting with lme4.  It would be nice eventually to
> do *internal* orthogonalization of the fixed-effects design
> matrix (or at least allow a switch for it), to make hand-centering/
> scaling/orthogonalization unnecessary, but for the time
> being there really are cases where centering matters.
> 
>  Ben Bolker
> 
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