Relative density clouds: Visualizing and exploring multivariate patterns of group differences
This paper introduces relative density clouds, a simple but powerful method to visualize the relative density of two groups in multivariate space. Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of...
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| Veröffentlicht in: | PloS one 2023-06, Vol.18 (6), p.e0287784 |
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| description | This paper introduces relative density clouds, a simple but powerful method to visualize the relative density of two groups in multivariate space. Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. An easy-to-use R function is provided to make this visualization method widely accessible to researchers. |
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Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. An easy-to-use R function is provided to make this visualization method widely accessible to researchers.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0287784</identifier><identifier>PMID: 37368918</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Biology and Life Sciences ; Cluster Analysis ; Cognition & reasoning ; Datasets ; Density ; Engineering and Technology ; Gender differences ; Health surveys ; Methods ; Multivariate analysis ; Neuropsychology ; Normal distribution ; Physical Sciences ; Population density ; Ratios ; Skewness ; Social Sciences ; Specific Gravity ; Variables ; Visualization (Computers)</subject><ispartof>PloS one, 2023-06, Vol.18 (6), p.e0287784</ispartof><rights>Copyright: © 2023 Marco Del Giudice. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Marco Del Giudice. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Marco Del Giudice 2023 Marco Del Giudice</rights><rights>2023 Marco Del Giudice. 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Relative density clouds employ k-nearest neighbor density estimates to provide information about group differences throughout the entire distribution of the variables. The method can also be used to decompose overall group differences into the specific contributions of differences in location, scale, and covariation. Existing relative distribution methods offer a flexible toolkit for the analysis of univariate differences; relative density clouds bring some of the same advantages to fruition in the context of multivariate research. They can assist in the exploration of complex patterns of group differences, and help break them down into simpler, more interpretable effects. 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| subjects | Analysis Biology and Life Sciences Cluster Analysis Cognition & reasoning Datasets Density Engineering and Technology Gender differences Health surveys Methods Multivariate analysis Neuropsychology Normal distribution Physical Sciences Population density Ratios Skewness Social Sciences Specific Gravity Variables Visualization (Computers) |
| title | Relative density clouds: Visualizing and exploring multivariate patterns of group differences |
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