[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

*To*: <asreml@chiswick.anprod.csiro.au>*Subject*: GLMMs*From*: Kim Bunter <kbunter@metz.une.edu.au>*Date*: Thu, 08 Jun 2000 17:21:37 +1000*Sender*: asreml-owner@lamb.chiswick.anprod.csiro.au

Dear Arthur, could you please provide a reference on the methodology used for your Binomial GLMM? I've just read a paper written by Engel et al (1995) discussing your Gilmour et al. (1985) procedure, but aren't sure if this is what you use in ASREML? I can't find reference to your exact procedure in the manual, but I guess by now you will not be using your early approach....? I have also read the various answers regarding aspects of running GLMMs (on-line and in the manual), and have a fairly general understanding of what's going on (I think). However, I am a bit confused as to exactly what information the Deviance value is telling you (and why you calculate it twice per iteration used to estimate the VC) and how this is related to dispersion. This probably just reflects my appalling understanding of GLMMs in general, but would appreciate it if you could help out with a laymans description of what exactly is going on (and I can read your reference material at leisure until I better understand it :-)). For example, it is my understanding that when your variance heterogenity factor (deviance/DF) is approximately 1 (as occurs under and animal model) there is no need to allow for overdispersion because it doesn't exist when cluster size=1 (ie don't re-run using !disp). Further, that the deviance is supposed to represent the goodness of fit of replacing y values with fitted values. I would have thought this also equated with maximising the information contained in the working variables for parameter estimation, but guess it doesn't account for the larger measurement error associated with the small cluster size (ie an animal) compared to larger cluster sizes (eg. a sire)? Hence, even though the deviance is smallest and disp is approx. 1 under an animal model, this in no way tells you that the estimate of additive variance (for example) is going to be the best of alternative models. In fact, the additive variance under an animal model is strongly biased downwards - not very helpful if you are wanting to fit an additive-maternal model. Further, this unaccounted for additive variance can be picked up by additional random effects which have inherently high sampling correlations with the additive effects (eg maternal). ie resulting in spurious maternal effects for example. It is also not possible to compare the Log-likelihoods of additive vs additive-maternal effect models (my simulations often show the incorrect direction of change under more parameters). Is this something to do with not being able to use deviance as an appropriate measure of fit when cluster size=1 (although cluster size presumably varies for the different effects: ie., = 1 for animal effects but is large for sire effects)? Have I got the right understanding of what deviance, variance heterogenity and dispersion mean in the context of interpreting output from ASREML? Is the deviance adjust a scaled deviance - or is your variance heterogenity (VH) value a scaled deviance (D/disp)? What if the implied dispersion is incorrect? It seems to me that after running with !disp (sire model), the error variance was altered but the VH value was pretty much the same anyway (ie, no improvement in fit - just rescaling?). Is this always the case with using !disp, and if so, why bother rescaling weights if the fit of the model is not improved? Also, regarding models where we want to estimate maternal effects (for example), what are the best options for 0/1 data? Anyway, no doubt I have just displayed how little I understand about GLMMs, but hopefully others will also benefit from your reply :-). Hope you are enjoying your visit OS (or are you back now?)... Cheers Kim Kim Bunter (M.Rur.Sc) PhD Student Animal Genetics and Breeding Unit University of New England Armidale, NSW, 2351 AUSTRALIA Ph (ISD): -61-2-67733788 Fax (ISD): -61-2-67733266 email: kbunter@metz.une.edu.au -- Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml

- Prev by Date:
**AS-REML for HP-UX environment** - Next by Date:
**Combining desigh variables** - Prev by thread:
**AS-REML for HP-UX environment** - Next by thread:
**Re: GLMMs** - Index(es):