Linear mixed model - heterogeneity (Etn bot) - R-SIG-mixed-models

Etn bot

Mon, Nov 2, 2015 3:04 AM #

@ Alain, many thanks for your response.... I will check out this type of
model

On 30 October 2015 at 07:35, Highland Statistics Ltd <highstat at highstat.com>
wrote:




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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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