ArticleJournal of applied statistics2020
Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets.
Article in Journal of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Learning-induced reorganization of number neurons and emergence of numerical representations in a biologically inspired neural network.Nature communications · 2023Article
- Quality Control in Metagenomics Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Transcriptomic analysis of resistance and short-term induction response to pyrethroids, in Anopheles coluzzii legs.BMC genomics · 2021Article
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Abstract
Metric multidimensional scaling (MDS) is a widely used multivariate method with applications in almost all scientific disciplines. Eigenvalues obtained in the analysis are usually reported in order to calculate the overall goodness-of-fit of the distance matrix. In this paper, we refine MDS goodness-of-fit calculations, proposing additional point and pairwise goodness-of-fit statistics that can be used to filter poorly represented observations in MDS maps. The proposed statistics are especially relevant for large data sets that contain outliers, with typically many poorly fitted observations, and are helpful for improving MDS output and emphasising the most important features of the dataset. Several goodness-of-fit statistics are considered, and both Euclidean and non-Euclidean distance matrices are considered. Some examples with data from demographic, genetic and geographic studies are shown.
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