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Generalized linear modelling for parasitologists

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Generalized linear modelling for parasitologists. / Wilson, Kenneth; Grenfell, Bryan T.
In: Parasitology Today, Vol. 13, No. 1, 01.01.1997, p. 33-38.

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

Harvard

Wilson, K & Grenfell, BT 1997, 'Generalized linear modelling for parasitologists', Parasitology Today, vol. 13, no. 1, pp. 33-38. https://doi.org/10.1016/S0169-4758(96)40009-6

APA

Wilson, K., & Grenfell, B. T. (1997). Generalized linear modelling for parasitologists. Parasitology Today, 13(1), 33-38. https://doi.org/10.1016/S0169-4758(96)40009-6

Vancouver

Wilson K, Grenfell BT. Generalized linear modelling for parasitologists. Parasitology Today. 1997 Jan 1;13(1):33-38. doi: 10.1016/S0169-4758(96)40009-6

Author

Wilson, Kenneth ; Grenfell, Bryan T. / Generalized linear modelling for parasitologists. In: Parasitology Today. 1997 ; Vol. 13, No. 1. pp. 33-38.

Bibtex

@article{d83764af587248feb1405a212f7c5e0c,
title = "Generalized linear modelling for parasitologists",
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.",
author = "Kenneth Wilson and Grenfell, {Bryan T.}",
year = "1997",
month = jan,
day = "1",
doi = "10.1016/S0169-4758(96)40009-6",
language = "English",
volume = "13",
pages = "33--38",
journal = "Parasitology Today",
issn = "0169-4758",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Generalized linear modelling for parasitologists

AU - Wilson, Kenneth

AU - Grenfell, Bryan T.

PY - 1997/1/1

Y1 - 1997/1/1

N2 - 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.

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.

U2 - 10.1016/S0169-4758(96)40009-6

DO - 10.1016/S0169-4758(96)40009-6

M3 - Journal article

AN - SCOPUS:0031025135

VL - 13

SP - 33

EP - 38

JO - Parasitology Today

JF - Parasitology Today

SN - 0169-4758

IS - 1

ER -