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

[Highland Statistics Ltd]

> ----------------------------------------------------------------------
>
> Message: 1
> Date: Thu, 23 Apr 2015 18:16:26 +0200
> From: Lize van der Merwe <lizestats at gmail.com>
> To: <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] nested mixed effects logistic regression binomial
> 	glm)	results differ by function.
> Message-ID: <009401d07de0$dba71190$92f534b0$@gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> 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?
Instead of focusing on the 'which one is correct' question, I would 
focus more on 'why are they different'. If two techniques give you 
rather different results then perhaps your model is too complicated, or 
your data set does not allow for a 2-way nested model. Simulate a data 
set with 2500 observations with nicely balanced clusters and see whether 
you have the same problems. Then cripple the data set (make unbalanced 
clusters) and see when differences occur. And yes....doing it in a 
Bayesian context is a good idea too.

Alain


>
> Thanks in advance.  (I posted this on another site too.)
>
> Lize





-- 
Dr. Alain F. Zuur

First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).

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