[R-sig-ME] estimating variance components for arbitrarily defined var/covar matrices

[Matthew Keller]

Hi all,

This has been wonderful to follow, thank you very much to all who have
contributed!!

Quick clarification:
Z*Z' is fixed/known. VG is unknown and would be estimated from the data.

Another issue:
The number of individuals fit in these models is often very large (e.g.,
10K - 100K) because the variance of the off-diagonals of Z*Z' is tiny. Of
the above approaches suggested, are any able to work with datasets of this
size in a 'reasonable' amount of time? E.g., < 1 day?

Best,

Matt

On Thu, Feb 26, 2015 at 2:47 PM, Rolf Turner <r.turner at auckland.ac.nz>
wrote:

> On 26/02/15 16:54, Ben Bolker wrote:
>
>> -----BEGIN PGP SIGNED MESSAGE-----
>> Hash: SHA1
>>
>>    I thought we were assuming a fixed var-cov matrix
>>
>
> So Z*Z'*VG is fixed/known, rather than being estimated from the data.
>
> That's what I didn't properly apprehend.
>
>  PLUS an error
>> variance, i.e. Sigma + s^2*I (increasing the variance and decreasing
>> the correlation).
>>
>>    But I could be wrong about what model is intended.
>>
>
> No, I think that the misunderstanding was entirely mine.
>
> Sorry for the noise.
>
> cheers,
>
> Rolf
>
> --
> Rolf Turner
> Technical Editor ANZJS
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
> Home phone: +64-9-480-4619
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
Matthew C Keller
Asst. Professor of Psychology
University of Colorado at Boulder
www.matthewckeller.com

	[[alternative HTML version deleted]]