[R-sig-ME] fixed observation-level variance in glmm

[Paul Buerkner]

If you are willing to go Bayesian, you can use the brms package. For your
model, the syntax would look as follows

brm(bf(y~(1|A)+(1|B), shape = cv), family = Gamma(link="identity"), ...)

Best,
Paul

2017-11-23 15:24 GMT+01:00 Coe, Richard (ICRAF) <R.COE at cgiar.org>:

> Dear mixed modellers
>
> I have a data set classified by two factors A and B both of which are
> random. There is also an observation-level (residual)  random term that has
> a known variance,  defined by a known constant cv.  Since the dispersion
> parameter in a Gamma glm is the  cv, I want to do something like:
>
> glmer(y~(1|A)+(1|B),  family=Gamma(link="identity"), dispersion = cv  )
> where cv is known.
>
> I can not see this facility or syntax in any of the mixed model tools I
> know of. Is there an easy way to fit this model?
>
> Thanks
> Ric
>
> Richard Coe
> Principal Scientist - Research Methods
> World Agroforestry Centre (ICRAF), Nairobi, Kenya
> and
> Statistics for Sustainable Development, Reading, UK
>
> Phone: +447734104196, +358503247733
>
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