Highland Statistics Ltd
highstat at highstat.com
Mon Feb 26 11:25:50 CET 2018
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?
<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.
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
End of R-sig-mixed-models Digest, Vol 134, Issue 36
Dr. Alain F. Zuur
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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).
More information about the R-sig-mixed-models