[R-sig-ME] semicontinuous variables: what likelihoods are available?

[Aurelie Cosandey Godin]

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 at ...> 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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