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Generalized additive modelling and zero inflated count data

Generalized additive modelling and zero inflated count data
Generalized additive modelling and zero inflated count data
This paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions. Zero inflated data are defined as data that has a larger proportion of zeros than expected from pure count (Poisson) data. The standard methodology is to model the data in two steps, first modelling the association between the presence and absence of a species and the available covariates and second, modelling the relationship between abundance and the covariates, conditional on the organism being present. The approach in this paper extends previous work to incorporate the use of Generalized Additive Models (GAM) in the modelling steps. The paper develops the link and variance functions needed for the use of GAM with zero inflated data. It then demonstrates the performance of the models using data on stem counts of Eucalyptus mannifera in a region of South East Australia.
abundance models, statistical models, count data, prediction, distribution modelling, zero inflated data, generalized additive models
0304-3800
179-188
Barry, Simon C.
e5d45e6c-94f1-4c2d-ba6c-9eec2c4aede8
Welsh, A.H.
27640871-afff-4d45-a191-8a72abee4c1a
Barry, Simon C.
e5d45e6c-94f1-4c2d-ba6c-9eec2c4aede8
Welsh, A.H.
27640871-afff-4d45-a191-8a72abee4c1a

Barry, Simon C. and Welsh, A.H. (2002) Generalized additive modelling and zero inflated count data. Ecological Modelling, 157 (2-3), 179-188. (doi:10.1016/S0304-3800(02)00194-1).

Record type: Article

Abstract

This paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions. Zero inflated data are defined as data that has a larger proportion of zeros than expected from pure count (Poisson) data. The standard methodology is to model the data in two steps, first modelling the association between the presence and absence of a species and the available covariates and second, modelling the relationship between abundance and the covariates, conditional on the organism being present. The approach in this paper extends previous work to incorporate the use of Generalized Additive Models (GAM) in the modelling steps. The paper develops the link and variance functions needed for the use of GAM with zero inflated data. It then demonstrates the performance of the models using data on stem counts of Eucalyptus mannifera in a region of South East Australia.

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More information

Published date: 2002
Keywords: abundance models, statistical models, count data, prediction, distribution modelling, zero inflated data, generalized additive models
Organisations: Statistics

Identifiers

Local EPrints ID: 29944
URI: http://eprints.soton.ac.uk/id/eprint/29944
ISSN: 0304-3800
PURE UUID: 09e04d8b-8847-4916-aa49-6f74892f0723

Catalogue record

Date deposited: 11 May 2006
Last modified: 15 Jul 2019 19:08

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