Generalized linear modelling for parasitologists

Kenneth Wilson; Bryan T. Grenfell

doi:10.1016/S0169-4758(96)40009-6

Generalized linear modelling for parasitologists

Kenneth Wilson
, Bryan T. Grenfell

Research output: Contribution to journal › Article › peer-review

218 Scopus citations

Abstract

Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical problem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type 1 errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.

Original language	English (US)
Pages (from-to)	33-38
Number of pages	6
Journal	Parasitology Today
Volume	13
Issue number	1
DOIs	https://doi.org/10.1016/S0169-4758(96)40009-6
State	Published - Jan 1997
Externally published	Yes

All Science Journal Classification (ASJC) codes

Parasitology

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10.1016/S0169-4758(96)40009-6

Cite this

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abstract = "Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical problem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type 1 errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.",

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AB - Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical problem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type 1 errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.

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Generalized linear modelling for parasitologists

Abstract

All Science Journal Classification (ASJC) codes

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