[R-sig-ME] Additive versus multiplicative overdispersion modeling

[Jarrod Hadfield]

Hi Shinichi,

Just tried - this works for me.

Cheers,

Jarrod
Quoting Shinichi Nakagawa <shinichi.nakagawa at otago.ac.nz>:

> Hi, Jarrod
>
> I think you can probably install lme4 on Mac - see the blog and its   
> correspondences (I have not tried myself).
>
> http://www.stat.columbia.edu/~cook/movabletype/archives/2010/08/multilevel_mode_11.html
>
> Thanks for the tip
>
> Best wishes
>
> Shinichi
>
> Shinichi Nakagawa, PhD
> (Lecturer of Behavioural Ecology)
> Department of Zoology
> University of Otago
> 340 Great King Street
> P. O. Box 56
> Dunedin, New Zealand
> Tel:  +64-3-479-5046
> Fax: +64-3-479-7584
> http://www.otago.ac.nz/zoology/staff/academic/nakagawa.html
> ________________________________________
> From: Jarrod Hadfield [j.hadfield at ed.ac.uk]
> Sent: Saturday, 21 August 2010 7:42 p.m.
> To: Ned Dochtermann
> Cc: David Duffy; r-sig-mixed-models at r-project.org; Holger   
> Schielzeth; Shinichi Nakagawa
> Subject: Re: [R-sig-ME] Additive versus multiplicative   
> overdispersion   modeling
>
> Hi Ned,
>
> You can get the additive residual term of N&S by fitting an
> observation-level random effect (i.e. one effect for each datum). You
> will need the latest version of lme4 for this (not available for Mac).
> If the data are binary you can't estimate the residual, so it is usual
> just to set it to zero.
>
> Cheers,
>
> Jarrod
>
>
> Quoting Ned Dochtermann <ned.dochtermann at gmail.com>:
>
>> 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
>>>
>>
>> _______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
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>
>
>



-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.