Additive versus multiplicative overdispersion modeling
Thanks a lot, if that is indeed the case it makes calculating repeatabilities per N&S quite straightforward for the multiplicative models (quasibinomial & quasipoisson) since the relevant term to include in the denominator would just be (summary(model)@sigma)^2 (multiplied by (pi^2)/3 ). Of course I still can't figure out how to get the needed information from the additive models, i.e. the residual of the distribution specific variance. Ned
On Thu, Aug 19, 2010 at 10:00 PM, David Duffy <davidD at qimr.edu.au> wrote:
On Thu, 19 Aug 2010, Ned Dochtermann wrote:
I am currently trying to calculate repeatability estimates (intra-class correlation coefficients) following Nakagawa & Schielzeth (2010, Biol.Rev. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. online early). The details of my models shouldn't be important except that I originally fit the models using binomial error structures and a logit link.
Nakagawa and Schielzeth (henceforth N&S) specify that repeatability estimates differ based on whether additive or multiplicative overdispersion modelling is conducted.
[SNIP]
These definitions are based on Browne et al. (2005, J. Roy. Stat. Soc A, 168:599-613). Based on my reading of the family objects description it seems that using the quasibinomial family would correspond to the multiplicative overdispersion modelling and the binomial family would correspond to additive overdispersion modelling.
Yes. ?Browne et al say they are using the "additive" approach because it has a proper likelihood. If you are interested in repeatability of binary measures, there are lots of perfectly good "direct" measures. ?The thing about the GLMM variance components is that they are up in the latent variable part of the model. If you are using a probit-normal, you are getting (essentially) tetrachoric correlations, that is, estimating the correlation between the "true" continuous measures that are being arbitrarily dichotomized to give you your binary outcome. ?For biometrical geneticists, this is a regarded as a good thing (Yule might disagree ;)), but might not be as useful for, say, assessing different clinical tests. ?It really does depend on your actual problem. Cheers, David Duffy. -- | 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