R help - Jul 2012 - classification using zero-inflated negative binomial mixture model

Hi,
   I want using zero-inflated negative binomial regression model to
classify data(a vector of data), that is I want know each observed value is
more likely belong to the "zero" or "count"
distribution(better with
relative probability). My data is some like:

   count site samp

 12909    1    1

   602     1    2

    50      1    3

  1218    1     4

 91291   1     5

while "count" is the data with a mixture of "zero" and
"non-zero"
distribution I want know, and "site", "samp" are two
prediction valuables
with additive effect(but I am not interested in it).

  I have tried the zeroinfl function of pscl package to fit zero-inflated
negative binomial regression. But it  can not give you the classification
result of "count".  Can anyone help with some indication of how to do
it or
other tools that can do this job ??


-- 
Kai Ying
Iowa State University
ISU, Ames IA 50010
Email: yingk@iastate.edu

	[[alternative HTML version deleted]]

Kai Ying <yingk <at> iastate.edu> writes:
> 
> Hi,
>    I want using zero-inflated negative binomial regression model to
> classify data(a vector of data), that is I want know each observed value is
> more likely belong to the "zero" or "count"
distribution(better with
> relative probability). My data is some like:
> 
>    count site samp
> 
>  12909    1    1
> 
>    602     1    2
> 
>     50      1    3
> 
>   1218    1     4
> 
>  91291   1     5
> 
> while "count" is the data with a mixture of "zero" and
"non-zero"
> distribution I want know, and "site", "samp" are two
prediction valuables
> with additive effect(but I am not interested in it).
> 
>   I have tried the zeroinfl function of pscl package to fit zero-inflated
> negative binomial regression. But it  can not give you the classification
> result of "count".  Can anyone help with some indication of how
to do it or
> other tools that can do this job ??
  Not sure, but you may be able to do this by hand.

  For a predicted mean value mu, overdispersion parameter k, and
zero-inflation probability p, the probability p_z of a structural zero
is p, while the probability of a sampling zero p_s is (k/(mu+k))^k ;
therefore the probability that an observed zero is a structural 
zero is p_z/(p_s+p_z) ...