[R-sig-ME] Residual variance random effect GLMM

Thierry Onkelinx thierry.onkelinx at inbo.be
Mon Jun 20 09:50:59 CEST 2016

Dear Sara,

Unlike a linear model, generalised linear models don't have a residual
variance. A linear model assumes a Gaussian distribution with two
parameters: mean and standard error which are independent. Generalised
linear models use distributions how dependent on only one parameter
(binomial, Poisson). Mean and variance of those distributions are defined
by the same parameter. In case a generalised linear model uses a two
parameter distribution (e.g. negative binomial), still the mean and
variance are influenced by a common parameter (mean = mu, var = mu + mu ^ 2

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-06-18 17:30 GMT+02:00 Fraixedas, Sara <sara.fraixedas op helsinki.fi>:

> Dear all,
> I want to calculate the percentage variance explained by a random effect
> in a GLMM fitted using the "glmmADMB" package. For that I would need to
> know what is the residual variance but it is not given in
> "VarCorr.glmmadmb" or in the summary output command.
> How can I extract the residual variance from a random effect in this
> particular case?
> Thank you in advance,
> Sara Fraixedas
> Doctoral Student
> The Helsinki Lab of Ornithology (HelLO) Finnish Museum of Natural
> History P.O. Box 17
> 00014 University of Helsinki, Finland
> Tel. +358-9-19128851
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