[R-sig-ME] Linear mixed model - heterogeneity (Etn bot)

[Highland Statistics Ltd]

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>
> Message: 1
> Date: Fri, 23 Oct 2015 15:15:45 +0100
> From: Etn bot <etnbot1 at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Linear mixed model - heterogeneity
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> 	<CAF79uvkRGaWXkzjPz9grTRhdQSVcqUmLrB+5QWUNS76JJLwmYg at mail.gmail.com>
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>
> I have a run a linear mixed effects model in R to model clinical data,
> however this model is heteroscedastic (as there excess zeros in the
> response variable)....
>
> I have tried transforming the data (log transform) and (sqrt), however
> neither transformation resolve the issue (see residual versus fitted value
> plot). I have not used cox proportional hazards model as the data is not
> time-to-event data, the data measures force and there are a large number of
> observations have a reading of zero. I cannot exclude these readings as
> they are valid.
>
> I have found a R package that runs Tobit regression (AER), however this
> will not accommodate the random effects in the model. I cannot find any R
> packages that run Weibull mixed effects models (or gamma mixed effects
> models)...
>
> Does anyone know if there is a package to run these type of models? (or can
> they suggest any alternative approach).
>
> Many thanks
>
>
> Etn

Hi,

Run a hurdle model that consists of:

1. A Logistic regression model on the absence/presence data (e.g. using 
glmer).
2. A Gamma GLMM on the presence only data

Then figure out the mean and variance of the zero altered Gamma 
distribution so that you have the fitted values of the combined model.

Alain

PS. This is also part of an exercise in the following course (which will 
run next week in Spain)..;-)

http://highstat.com/Courses/Flyers/Flyer2015_11Elche.pdf





-- 
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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