[R-sig-ME] zero-inflated-count-data?

[Thierry Onkelinx]

Another option is the fit the model using a distribution without
zero-inflation. Then simulate data from that model and count the number of
zero's. Repeat this several times so that you get a distribution of the
number of zero's. In case of zero-inflation the number of zero's in the
data is much higher that those from the simulated data.

Best regards,


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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2018-02-25 23:55 GMT+01:00 Jonathan Judge <bachlaw01 at outlook.com>:

> The pscl package offers the (somewhat controversial) Vuong test for this
> purpose and is a good ZI/hurdle resource in general.
>
> Jonathan
>
> > On Feb 25, 2018, at 3:45 PM, C. AMAL D. GLELE <altessedac2 at gmail.com>
> wrote:
> >
> > Hi, Ben
> > Many thanks to you for your very helpful reply.
> > Best and regards,
> >
> > 2018-02-25 19:05 GMT+01:00 Ben Bolker <bbolker at gmail.com>:
> >
> >> There is no set proportion.  (For example, a Poisson distribution with
> >> a mean of 0.01 is expected to be about 99% zeros, even without
> >> zero-inflation.) There's a little bit of (bare-bones) discussion of
> >> how to test for zero-inflation here:
> >> https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#zero-inflation
> >>
> >> On Sun, Feb 25, 2018 at 7:08 AM, C. AMAL D. GLELE <
> altessedac2 at gmail.com>
> >> wrote:
> >>> Hi, dear all.
> >>> From which proportion of zero a count should be considered as
> >> zero-inflated
> >>> (in order to use a zero-inflated model for it's modelling)?
> >>> In advance, thanks for your replies.
> >>> Best,
> >>>
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