[R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.

[Linus Holtermann]

Dear Lize,

maybe you give Bayesian methods a try. The excellent MCMCglmm package should be able to handle your model. Often MCMC provides more reliable results when a wide range of variation in group size and relative small number of observations per group are present in the data.

Best regards,

Linus Holtermann
Hamburgisches WeltWirtschaftsInstitut gemeinnützige GmbH (HWWI)
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-----Ursprüngliche Nachricht-----
Von: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] Im Auftrag von Thierry Onkelinx
Gesendet: Freitag, 24. April 2015 09:43
An: Lize van der Merwe
Cc: r-sig-mixed-models at r-project.org
Betreff: Re: [R-sig-ME] nested mixed effects logistic regression binomial glm) results differ by function.

Dear Lize,

glmmPQL() uses Penalized Quasi-Likelihood and glmer() uses the likelihood in case of a binomial family. I prefer methods that uses the likelihood.

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

2015-04-23 18:16 GMT+02:00 Lize van der Merwe <lizestats at gmail.com>:

> Please advise:
>
> I have a dichotomous outcome on 2500 individuals. From 18 geographical 
> areas, and many households nested within areas. I need to assess the 
> association between various predictors and my outcome, adjusting for 
> the correlation within households, as well as within areas. The 
> following R functions provide dramatically different results.
>
> glmer(CC~predictor+1|area/household,family=binomial)
>
> and
>
> glmmPQL(CC~predictor, random=~1|area/household),family=binomial)
>
> Why? Which is correct?
>
> Thanks in advance.  (I posted this on another site too.)
>
> Lize
>
>
>
>
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>
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