Linear mixed model - heterogeneity

lme4 will run Gamma mixed models, but these don't accomodate zeros.  I
don't think Weibull will either.  You're also right that
transformation won't generally solve these problems. There are very
few positive distributions, not considering censored variants of
real-valued distributions, that will naively allow zeros.   You could
run a two-stage model (Bernoulli model for zero vs non-zero, then a
positive-distribution model for the conditional effects on the
non-zero values only).

The cplm package allows tweedie mixed models, which might work for
you. AD Model Builder and Template Model Builder will allow you to fit
fixed models from any distribution you can specify (with a generic
Laplace approximation engine built in), but the learning curve is
pretty steep ...

It's important in this case to consider the source of your zeros.  Are
they below minimal detection limits (in which case something like a
Tobit is appropriate)?  Do they represent a separate process (in which
case two-stage models are sensible)? Or ... ?

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

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