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A non‐parametric permutation method for assessing agreement for distance matrix observations
Author(s) -
Røislien Jo,
Samset Eigil
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5927
Subject(s) - hierarchical clustering , permutation (music) , permutation matrix , distance matrices in phylogeny , parametric statistics , matrix (chemical analysis) , cluster analysis , replication (statistics) , distance matrix , random matrix , mathematics , computer science , algorithm , statistics , combinatorics , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , circulant matrix , acoustics , composite material
Distance matrix data are occurring ever more frequently in medical research, particularly in fields such as genetics, DNA research, and image analysis. We propose a non‐parametric permutation method for assessing agreement when the data under study are distance matrices. We apply agglomerative hierarchical clustering and accompanying dendrograms to visualize the internal structure of the matrix observations. The accompanying test is based on random permutations of the elements within individual matrix observations and the corresponding matrix mean of these permutations. We compare the within‐matrix element sum of squares (WMESS) for the observed mean against the WMESS for the permutation means. The methodology is exemplified using simulations and real data from magnetic resonance imaging. Copyright © 2013 John Wiley & Sons, Ltd.