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Titolo:
A new method for non-parametric multivariate analysis of variance
Autore:
Anderson, MJ;
Indirizzi:
Univ Sydney, Marine Ecol Labs A11, Ctr Res Ecol Impacts Coastal Cities, Sydney, NSW 2006, Australia Univ Sydney Sydney NSW Australia 2006 Cities, Sydney, NSW 2006, Australia
Titolo Testata:
AUSTRAL ECOLOGY
fascicolo: 1, volume: 26, anno: 2001,
pagine: 32 - 46
SICI:
1442-9985(200102)26:1<32:ANMFNM>2.0.ZU;2-1
Fonte:
ISI
Lingua:
ENG
Soggetto:
URBAN MANGROVE FORESTS; PERMUTATION TEST; RAPID ASSESSMENT; LINEAR-MODEL; ECOLOGY; VARIABILITY; ASSEMBLAGES; COMMUNITY; DESIGN; RANDOMIZATION;
Keywords:
ANOVA; distance measure; experimental design; linear model; multifactorial; multivariate dissimilarity; partitioning; permutation tests; statistics;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
80
Recensione:
Indirizzi per estratti:
Indirizzo: Anderson, MJ Univ Auckland, Dept Stat, Private Bag 92019, Auckland 1, New Zealand Univ Auckland Private Bag 92019 Auckland New Zealand 1 aland
Citazione:
M.J. Anderson, "A new method for non-parametric multivariate analysis of variance", AUSTRAL EC, 26(1), 2001, pp. 32-46

Abstract

Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however,are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description provided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test-statistic is a multivariate analogue to Fisher's F-ratio and is calculated directlyfrom any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.

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Documento generato il 13/08/20 alle ore 13:27:44