2012
DOI: 10.1111/j.2041-210x.2012.00190.x
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mvabund– an R package for model‐based analysis of multivariate abundance data

Abstract: Summary1. The mvabund package for R provides tools for model-based analysis of multivariate abundance data in ecology. 2. This includes methods for visualising data, fitting predictive models, checking model assumptions, as well as testing hypotheses about the community-environment association.3. This paper briefly introduces the package and demonstrates its functionality by example.

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Cited by 1,321 publications
(1,220 citation statements)
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References 14 publications
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“…Bacterial OTUs correlated with seawater, whale blow or air samples were identified using Analysis of Variance (ANOVA) based on generalized linear models fit to the data (Wang et al, 2012). OTU diversity and abundance profiles for air and whale blow were significantly different (p < 0.05) from each other and bear little similarity with communities characteristic of the adjacent seawater.…”
Section: Next Generation Sequencing Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Bacterial OTUs correlated with seawater, whale blow or air samples were identified using Analysis of Variance (ANOVA) based on generalized linear models fit to the data (Wang et al, 2012). OTU diversity and abundance profiles for air and whale blow were significantly different (p < 0.05) from each other and bear little similarity with communities characteristic of the adjacent seawater.…”
Section: Next Generation Sequencing Resultsmentioning
confidence: 99%
“…The community structure dissimilarity between samples was observed with non-metric multidimensional scaling. Significant differences in communities sampled in seawater, UAV-captured air or whale blow samples were defined using generalized linear models within mvabund (Wang et al, 2012). Briefly, a negative binomial model was fit to the OTU abundance data and the sample grouping was analyzed using Analysis of Variance (ANOVA).…”
Section: Data Analysesmentioning
confidence: 99%
“…An in-depth multivariate analysis was performed in order to evaluate dispersion effects between test groups by using the R package Bmvabund^selecting the Banova.manyglm^call. After checking model assumptions by plotting Bmeanvar^and Bmanyglmm atrixes, data was further processed as a Bnegative binomial^distribution, a typical option for abundance count data in the mvabund-package (Wang et al 2012). …”
Section: Resultsmentioning
confidence: 99%
“…Analyses were performed using the R‐package mvabund (Wang, Neuman, Wright, & Warton, 2012). The response variable was binary (infective/noninfective), hence we used a binomial distribution of errors.…”
Section: Methodsmentioning
confidence: 99%