Modelling of correlated soil animals count data

dc.contributor.authorDebusho, Legesse Kassa
dc.contributor.authorSileshi, Gudeta W.
dc.contributor.emaillegesse.debusho@up.ac.zaen_US
dc.date.accessioned2013-07-16T12:26:42Z
dc.date.available2013-07-16T12:26:42Z
dc.date.issued2012-11
dc.description.abstractEcological studies naturally result in correlated data. Ignoring these correlations can result in biased estimation of ecological effects jeopardizing the integrity of the scientific inference. Mixed effects models are likely to appeal to ecologists for handling correlated data (e.g. Sileshi, 2008), however careful consideration must be given to the interpretation of the parameter estimates from generalized linear mixed effects models with non-identity link functions. The objective of this study was to compare the generalized estimating equations (GEE) under different correlation structures and suggest appropriate models to describe the relationship between soil animal counts and covariates. The GEE with independence, exchangeable and AR1 correlation structures were compared using count data set of ants from soils under the agroforestry systems in eastern Zambia. The GEE model with AR1 correlation structure gave a better description of the data than did the independence and exchangeable correlation structures.en_US
dc.description.librarianam2013en_US
dc.description.urihttp://www.sastat.org.za/journal.htmen_US
dc.identifier.citationDebusho, LK & Sileshi, GW 2012, 'Modelling of correlated soil animals count data', South African Statistical Journal, no. sp 1, pp. 75-82.en_US
dc.identifier.issn0038-271X
dc.identifier.urihttp://hdl.handle.net/2263/21971
dc.language.isoenen_US
dc.publisherSouth African Statistical Associationen_US
dc.rightsSouth African Statistical Associationen_US
dc.subject.lcshSoil animalsen
dc.titleModelling of correlated soil animals count dataen
dc.typeArticleen

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