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
fixed observation-level variance in glmm
2 messages · Coe, Richard (ICRAF), 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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