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

[Fraixedas, Sara]

Dear Thierry,

Many thanks for your reply. You are totally right (I should have checked my notes more carefully before posting this question...).

Thanks for the clarification!


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

________________________________
From: Thierry Onkelinx <thierry.onkelinx at inbo.be>
Sent: 20 June 2016 09:50:59
To: Fraixedas, Sara
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Residual variance random effect GLMM

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 /theta).

Best regards,

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

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 at helsinki.fi<mailto:sara.fraixedas at 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<tel:%2B358-9-19128851>

        [[alternative HTML version deleted]]

_______________________________________________
R-sig-mixed-models at r-project.org<mailto:R-sig-mixed-models at r-project.org> mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


	[[alternative HTML version deleted]]