Chain Reversion for Detecting Associations in Interacting Variables—St. Nicolas House Analysis
| Autor: | Detlef Groth, Christian Aßmann, Michael Hermanussen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
association chains
Rank (linear algebra) Health Toxicology and Mutagenesis media_common.quotation_subject Association (object-oriented programming) lcsh:Medicine Bivariate analysis 01 natural sciences Article Correlation 010104 statistics & probability 03 medical and health sciences Statistics 0101 mathematics Spurious relationship 030304 developmental biology Mathematics Statistical hypothesis testing media_common 0303 health sciences Variables data matrices lcsh:R Public Health Environmental and Occupational Health Linear model bivariate correlation coefficients network graphs St. Nicolas House Analysis |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 18, Iss 1741, p 1741 (2021) International Journal of Environmental Research and Public Health Volume 18 Issue 4 |
| ISSN: | 1661-7827 1660-4601 |
| Popis: | (1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|