Wiley StatsRef: Statistics Reference Online· 2017
Permutational Multivariate Analysis of Variance ( <scp>PERMANOVA</scp> )
- 1,845citations
- 2017year
Short summary
PERMANOVA is a distribution-free method for partitioning variation in multivariate data based on a chosen dissimilarity measure, using permutations for statistical inference.
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Key points
- PERMANOVA partitions variation in multivariate data using a chosen dissimilarity measure.
- Statistical inferences are made using distribution-free permutation algorithms.
- The framework accommodates complex designs like random effects, hierarchical models, and unbalanced data.
- It can also model heterogeneous dispersions among groups.
- Ordination plots can visualize PERMANOVA results, including main effects and interactions.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Permutational multivariate analysis of variance (PERMANOVA) is a geometric partitioning of variation across a multivariate data cloud, defined explicitly in the space of a chosen dissimilarity measure, in response to one or more factors in an analysis of variance design. Statistical inferences are made in a distribution‐free setting using permutational algorithms. The PERMANOVA framework is readily extended to accommodate random effects, hierarchical models, mixed models, quantitative covariates, repeated measures, unbalanced and/or asymmetrical designs, and, most recently, heterogeneous dispersions among groups. Plots to accompany PERMANOVA models include ordinations of either fitted or residualized distance matrices, including multivariate analogues to main effects and interaction plots, to visualize results.
The authors' abstract, as published at the source. Wiley StatsRef: Statistics Reference Online, 2017 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences