[R-sig-ME] UNSOLVED: Re: lme4 or other open source mixed model package code equivalent to asreml-R

[David Duffy]

On Fri, 18 Nov 2011, Ben Bolker wrote:

> David Duffy wrote:
>> You might look at the regress and spatialCovariance packages of David
>> Clifford.  The former allows fitting of (Gaussian) mixed models where you
>> can specify the structure of the covariance matrix very flexibly (for
>> example, I have used it for pedigree data).  The spatialCovariance package
>> uses regress to provide more elaborate models than AR1xAR1, but may be
>> applicable.  You may have to correspond with the author about applying it
>> to your exact problem.
>
>  Hmm.  I took a quick look; it is nice to see another implementation
> of mixed effects models (you can never have too many, especially when
> they're open and can build on each other ...) -- BUT -- it's not
> immediately obvious to me (maybe this is the "correspond with the author"
> part?) how to construct this problem so that the variance structure
> corresponds to a sum of specified Gaussian values.  In particular, would
> we have to use an outer loop to profile over different values of the
> scale parameter (or run a 1-dimensional minimum-finder for the
> negative log-likelihood) ?

Well, I do not have any experience carrying out spatial modelling, but it 
did seem to me that the covariances arising from the separable AR1xAR1 
model must be have a close resemblance to some of the other standard 
models.  Specifically, page 85 of Haskard's thesis

http://digital.library.adelaide.edu.au/dspace/bitstream/2440/47972/1/02whole.pdf

seems to imply it is equivalent to a anisotropic Matern model.  This is 
offered by the spatialCovariance package, AIUI.

It took me a while to understand that the example dataset is the Wheat2 
data from the nmle package, which Pinheiro and Bates (2000, P 263) analyse 
using spherical and rational quadratic models.  I am unsure if the OP is 
particularly interested in the exact models he chose for his example, or 
whether one can generally match ASREML's functionality, or has data that 
might be comfortably analysed usibg existing lme correlation structures.

Cheers, David.

-- 
| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v