Data from: "Size" and "shape" in the measurement of multivariate proximity ...

• Main Author: Greenacre, Michael
• Format: Dataset
• Language: English
• Published: Dryad 2018
• Subjects: visualization; correspondence analysis; Euclidean distance; Ordination; Bray-Curtis dissimilarity; Cluster analysis; logarithmic transformation; Multivariate analysis; chi-square distance; Barents Sea; Bray
• Online Access: https://dx.doi.org/10.5061/dryad.6r5j8; https://datadryad.org/stash/dataset/doi:10.5061/dryad.6r5j8

1. Ordination and clustering methods are widely applied to ecological data that are nonnegative, for example species abundances or biomasses. These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as: (i) ordinations of the units, where interpoint distances optimally display the measured differences; (ii) clustering the units into homogeneous clusters; or (iii) assessing differences between pre-specified groups of units (e.g., regions, periods, treatment-control groups). 2. These methods all conceal a fundamental question: To what extent are the differences between the sampling units, computed according to the chosen proximity function, capturing the "size" in the multivariate observations, or their "shape"? "Size" means the overall level of the measurements: for example, some samples contain higher total abundances or more biomass, others less. "Shape" means the relative levels of the ... : Barents Sea fish abundancesAbundance counts of 41 fish species at 158 stations in the Barents Sea. This is a subset of a larger data set that spans more stations and years.fish.txtExperimental dataThree samples of size n=10 each, one from a control and two from two treatments. Each sample unit consists of abundances of 45 marine species.experiment.txt ...