[R-sig-ME] semiparametric mixed models
David Duffy
davidD at qimr.edu.au
Mon Mar 7 13:09:03 CET 2011
a couple of recent queries about appropriate transformations of Y
variables for mixed models reminded me of the approach used in
http://www.bios.unc.edu/~lin/publications/2006/DiaoLin06b.pdf
http://www.bios.unc.edu/~lin/publications/2005/DiaoLin05.pdf
The only software I know of:
http://www.bios.unc.edu/~lin/software/SVCC/
It's a little bit different, AIUI, from the various spline based
approaches that are available in R. It gave pretty similar results to
analysing Y after choosing a transformation based on a Box-Cox regression
ignoring any clustering in my one major use of it (but it did do it
automatically ;)). The approach can also be used for mixed modelling of
survival data, but we didn't get very far with their software for that.
Maybe a little R project for someone?
Cheers, David Duffy.
PS Actually, one could probably use coxme() for this.
PPS Also some of the programs that fit a linear mixed model for genetic
data allow one to include a power transformation (a la Box-Cox) in the
likelihood to be maximized (PAP comes to mind). I know of no evidence
that this is a particularly good idea. Folks from a biometrical genetic
background will know that some variance components in standard models for
pedigree data, eg genetic dominance, can be made to go to zero by such a
transformation of the phenotype.
--
| 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
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