Linear mixed model - heterogeneity

You may also try the brms package, which has a hurdle_gamma family that
might be helpful to you.

A sample hurdle_gamma model (using the epilepsy data of brms) may look
like this:

fit <- brm(count ~ 0 + trait * (log_Age_c + log_Base4_c * Trt_c)
            + (0+trait||patient),
            data = epilepsy, family = hurdle_gamma("log"))

The reserved variable "trait" has to levels, one for the gamma part and
one for the bernoulli part modeling zeros.

Currently, hurdle_gamma models are only available in the github version of
brms to be installed via

library(devtools)
install_github("paul-buerkner/brms")

Since brms is based on Stan, you will need a C++ compiler. Instructions on
how to get one are presented at the end of the README on
https://github.com/paul-buerkner/brms.



2015-10-23 16:15 GMT+02:00 Etn bot <etnbot1 at gmail.com>:

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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