Hi Aurelie,

I am probably more seeking assurance from the gurus than trying to answer
your question, as I'm also playing with a data set in a similar situation.
(I am looking at area of leaf consumed by insect herbivores.) Can you use a
delta-distribution/approach for your data? That is, run binomial models on
the presence-absence data of your response variable, and log-normal models
on the positive (non-zero) portion of your continuous data.

I know this approach is fairly common for linear models, e.g.
http://r-project.markmail.org/search/?q=delta%20Tweedie#query:delta%20Tweedie+page:1+mid:gnzpixld5zkl5sig+state:results

and I imagine it's equally applicable for (G)LMM's. I'll let the more
knowledgeable members of this list correct me if not. I didn't see any
attachment in your last message, so I don't know how your data are
distributed, but this approach seemed to work well for my data (with ~70%
zeros).

HTH,

George


On Mon, Mar 18, 2013 at 4:25 PM, Aurelie Cosandey Godin <GodinA@dal.ca>wrote:

> Thank you Ben and others,
>
> Apologize for not being very precise!
> My response variable is measured both in weight (kg) and counts and is
> very zero-inflated i.e., 91% of my data.
> I previously ran models on the count data using a suit of  likelihoods:
> 2-parts zero inflated poisson & 2-parts zero inflated negative binomial.
> The latter were the best.
> Now I would like to run the same models but with my response variable in
> kg, but I don't know how to model my positive (truncated or just positive
> weight data?). See  figure attached of the distribution of my weight data.
>
> Many thanks in advance!!
> Aurelie
>
>
>
>
>
>
> On 2013-03-18, at 4:30 PM, Ben Bolker wrote:
>
> > Aurelie Cosandey Godin <GodinA@...> writes:
> >
> >
> > [snip]
> >
> >> I need to run spatio-temporal models for a semicontinuous response
> >> variable (weight in kg).  I am not familiar with the available
> >> semicontinuous likelihood functions available in R and was wondering
> >> if some of you may be able to point me in the right direction for
> >> information.
> >
> >
> > Can you say any more about exactly what a semicontinuous response
> variable
> > is?  Poking around (e.g <
> http://lpsolve.sourceforge.net/4.0/semi-cont.htm>)
> > doesn't make it entirely clear: are these data that are
> >
> > truncated, i.e. values <= a lower threshold are absent from the data set;
> > censored, i.e. values <= a lower threshold are recorded as
> >   "less than threshold"?
> > positive, i.e. values <0 don't even exist?
> > are the data non-negative (i.e. >=0) or are they positive (>0)?
> >
> >  The simplest of these cases is positive data, which you
> > can model fairly easily by log transformation (i.e. assume
> > a lognormal distribution), or with slightly more difficulty
> > using a Gamma distribution ...  if you have censored or truncated
> > data, or data that include zeros, it gets a little harder ...
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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