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Final published version

Research output: Contribution to Journal/Magazine › Review article › peer-review

Published

In: Functional Ecology, Vol. 10, No. 5, 01.10.1996, p. 592-601.

Research output: Contribution to Journal/Magazine › Review article › peer-review

Wilson, K, Grenfell, BT & Shaw, DJ 1996, 'Analysis of aggregated parasite distributions: A comparison of methods', *Functional Ecology*, vol. 10, no. 5, pp. 592-601. https://doi.org/10.2307/2390169

Wilson, K., Grenfell, B. T., & Shaw, D. J. (1996). Analysis of aggregated parasite distributions: A comparison of methods. *Functional Ecology*, *10*(5), 592-601. https://doi.org/10.2307/2390169

Wilson K, Grenfell BT, Shaw DJ. Analysis of aggregated parasite distributions: A comparison of methods. Functional Ecology. 1996 Oct 1;10(5):592-601. doi: 10.2307/2390169

@article{46ed50008bd842ebaa46bc31165193a4,

title = "Analysis of aggregated parasite distributions: A comparison of methods",

abstract = "1. Empirically, parasite distributions are often best described by the negative binomial distribution; some hosts have many parasites while most have just a few. Thus identifying heterogeneities in parasite burdens using conventional parametric methods is problematical. In an attempt to conform to the assumptions of parametric analyses, parasitologists and ecologists frequently log-transform their overdispersed data prior to analysis. In this paper, we compare this method of analysis with an alternative, generalized linear modelling (GLM), approach. 2. We compare the classical linear model using log-transformed data (Model 1) with two GLMs: one with Poisson errors and an empirical scale parameter (Model 2), and one in which negative binomial errors are explicitly defined (Model 3). We use simulated datasets and empirical data from a long-term study of parasitism in Soay Sheep on St Kilda to test the efficacies of these three statistical models. 3. We conclude that Model 1 is much more likely to produce type I errors than either of the two GLMs, and that it also tends to produce more type II errors. Model 3 is only marginally more successful than Model 2, indicating that the use of an empirical scale parameter is only slightly more likely to generate errors than using an explicitly defined negative binomial distribution. Thus, while we strongly recommend the use of GLMs over conventional parametric analyses, either GLM method will serve equally well.",

keywords = "Aggregation, Generalized linear modelling, GLIM, Macroparasites, Negative binomial, Splus",

author = "K. Wilson and Grenfell, {B. T.} and Shaw, {D. J.}",

year = "1996",

month = oct,

day = "1",

doi = "10.2307/2390169",

language = "English",

volume = "10",

pages = "592--601",

journal = "Functional Ecology",

issn = "0269-8463",

publisher = "Blackwell Publishing Ltd",

number = "5",

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T1 - Analysis of aggregated parasite distributions

T2 - A comparison of methods

AU - Wilson, K.

AU - Grenfell, B. T.

AU - Shaw, D. J.

PY - 1996/10/1

Y1 - 1996/10/1

N2 - 1. Empirically, parasite distributions are often best described by the negative binomial distribution; some hosts have many parasites while most have just a few. Thus identifying heterogeneities in parasite burdens using conventional parametric methods is problematical. In an attempt to conform to the assumptions of parametric analyses, parasitologists and ecologists frequently log-transform their overdispersed data prior to analysis. In this paper, we compare this method of analysis with an alternative, generalized linear modelling (GLM), approach. 2. We compare the classical linear model using log-transformed data (Model 1) with two GLMs: one with Poisson errors and an empirical scale parameter (Model 2), and one in which negative binomial errors are explicitly defined (Model 3). We use simulated datasets and empirical data from a long-term study of parasitism in Soay Sheep on St Kilda to test the efficacies of these three statistical models. 3. We conclude that Model 1 is much more likely to produce type I errors than either of the two GLMs, and that it also tends to produce more type II errors. Model 3 is only marginally more successful than Model 2, indicating that the use of an empirical scale parameter is only slightly more likely to generate errors than using an explicitly defined negative binomial distribution. Thus, while we strongly recommend the use of GLMs over conventional parametric analyses, either GLM method will serve equally well.

AB - 1. Empirically, parasite distributions are often best described by the negative binomial distribution; some hosts have many parasites while most have just a few. Thus identifying heterogeneities in parasite burdens using conventional parametric methods is problematical. In an attempt to conform to the assumptions of parametric analyses, parasitologists and ecologists frequently log-transform their overdispersed data prior to analysis. In this paper, we compare this method of analysis with an alternative, generalized linear modelling (GLM), approach. 2. We compare the classical linear model using log-transformed data (Model 1) with two GLMs: one with Poisson errors and an empirical scale parameter (Model 2), and one in which negative binomial errors are explicitly defined (Model 3). We use simulated datasets and empirical data from a long-term study of parasitism in Soay Sheep on St Kilda to test the efficacies of these three statistical models. 3. We conclude that Model 1 is much more likely to produce type I errors than either of the two GLMs, and that it also tends to produce more type II errors. Model 3 is only marginally more successful than Model 2, indicating that the use of an empirical scale parameter is only slightly more likely to generate errors than using an explicitly defined negative binomial distribution. Thus, while we strongly recommend the use of GLMs over conventional parametric analyses, either GLM method will serve equally well.

KW - Aggregation

KW - Generalized linear modelling

KW - GLIM

KW - Macroparasites

KW - Negative binomial

KW - Splus

U2 - 10.2307/2390169

DO - 10.2307/2390169

M3 - Review article

AN - SCOPUS:0030303333

VL - 10

SP - 592

EP - 601

JO - Functional Ecology

JF - Functional Ecology

SN - 0269-8463

IS - 5

ER -