The depiction of linear association by matroids

Greene, Tom

doi:10.1016/0167-9473(90)90108-t

Cited by 1 publication

(1 citation statement)

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“…We propose another method in the matroid approach that could provide a compromise, whilst retaining the interpretational benefits associated with producing distinct non-overlapping clusters. Suggested by Greene [32] , the method draws from existing successful ideas in the field of collinearity diagnostics and cluster analysis, whilst also introducing favourable properties of matroids, which have previously been confined largely to theoretical work.…”

Section: Methodsmentioning

confidence: 99%

A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome

Woolston

Tu²,

Baxter³

et al. 2012

PLoS ONE

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BackgroundExploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst maintaining intuitive advantages of clustering methodology.MethodsTwo advanced techniques of clustering in the VARCLUS and matroid methods are discussed and implemented on a metabolic syndrome data set to analyze the structure of ten metabolic risk factors. The subjects were selected from the normative aging study based in Boston, Massachusetts. The sample included a total of 847 men aged between 21 and 81 years who provided complete data on selected risk factors during the period 1987 to 1991.ResultsFour core components were identified by the clustering methods. These are labelled obesity, lipids, insulin resistance and blood pressure. The exploratory factor analysis with oblique rotation suggested an overlap of the loadings identified on the insulin resistance and obesity factors. The VARCLUS and matroid analyses separated these components and were able to demonstrate associations between individual risk factors.ConclusionsAn oblique rotation can be selected to reflect the clinical concept of a single underlying syndrome, however the results are often difficult to interpret. Factor loadings must be considered along with correlations between the factors. The correlated components produced by the VARCLUS and matroid analyses are not overlapped, which allows for a simpler application of the methodologies and interpretation of the results. These techniques encourage consistency in the interpretation whilst remaining faithful to the construct under study.

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Section: Methodsmentioning

confidence: 99%