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