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

[Highland Statistics Ltd]

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Message: 5
Date: Mon, 26 Feb 2018 09:16:55 +0100
From: Thierry Onkelinx <thierry.onkelinx at inbo.be>
To: Jonathan Judge <bachlaw01 at outlook.com>
Cc: "C. AMAL D. GLELE" <altessedac2 at gmail.com>,  R SIG Mixed Models
	<r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] zero-inflated-count-data?
Message-ID:
	<CAJuCY5yAvWYG2YA8FgVV1urK7Q8E55eNuR=gYcKokxDBN9Jiog at mail.gmail.com>
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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.





I think that Thierry's suggestion is indeed the best option. It not only allows you to check whether
the model can cope with the observed number of zeros, but it also shows you to check whether the model can cope with other
aspects of the observed data. For example, you can calculate the frequency table for each of the 1000 simulated data sets,
and calculate somehow and average frequency table. And the compare this with the frequency table of the observed data.

Kind regards,
Alain










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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<https://www.inbo.be>


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End of R-sig-mixed-models Digest, Vol 134, Issue 36
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-- 

Dr. Alain F. Zuur
Highland Statistics Ltd.
9 St Clair Wynd
AB41 6DZ Newburgh, UK
Email: highstat at highstat.com
URL:   www.highstat.com

And:
NIOZ Royal Netherlands Institute for Sea Research,
Department of Coastal Systems, and Utrecht University,
P.O. Box 59, 1790 AB Den Burg,
Texel, The Netherlands



Author of:
1. Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA. (2017).
2. Beginner's Guide to Zero-Inflated Models with R (2016).
3. Beginner's Guide to Data Exploration and Visualisation with R (2015).
4. Beginner's Guide to GAMM with R (2014).
5. Beginner's Guide to GLM and GLMM with R (2013).
6. Beginner's Guide to GAM with R (2012).
7. Zero Inflated Models and GLMM with R (2012).
8. A Beginner's Guide to R (2009).
9. Mixed effects models and extensions in ecology with R (2009).
10. Analysing Ecological Data (2007).