Resemblance Profiles as Clustering Decision Criteria: Estimating Statistical Power, Error, and Correspondence for a Hypothesis Test for Multivariate Structure

Authors

Joshua P. Kilborn, University of South FloridaFollow
David L. Jones, University of South Florida
Ernst B. Peebles, University of South FloridaFollow
David F. Naar, University of South FloridaFollow

Document Type

Article

Publication Date

2017

Keywords

constrained clustering, data simulation, Monte Carlo, permutation testing, PRIMER-E, SIMPROF

Digital Object Identifier (DOI)

https://doi.org/10.1002/ece3.2760

Abstract

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Rights Information

This work is licensed under a Creative Commons Attribution 4.0 License.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Ecology and Evolution, v. 7, issue 7, p. 2039-2057

Scholar Commons Citation

Kilborn, Joshua P.; Jones, David L.; Peebles, Ernst B.; and Naar, David F., "Resemblance Profiles as Clustering Decision Criteria: Estimating Statistical Power, Error, and Correspondence for a Hypothesis Test for Multivariate Structure" (2017). Marine Science Faculty Publications. 1586.
https://digitalcommons.usf.edu/msc_facpub/1586