Title: Revisiting the concept of a symmetric index of agreement for continuous datasets
Citation: SCIENTIFIC REPORTS vol. 6 p. 19401
Publication Year: 2016
JRC N°: JRC98230
ISSN: 2045-2322
URI: http://www.nature.com/articles/srep19401
DOI: 10.1038/srep19401
Type: Articles in periodicals and books
Abstract: Quantifying how close two datasets are to each other is a common thing to do in scientific research. The Pearson product-moment correlation coefficient r is a widely used measure of the strength between two data series but gives no indication of how similar these are in magnitude. Although a number of indexes have been proposed to compare a dataset with a reference, only few are available to compare two datasets of equivalent (or unknown) reliability. After a brief review and numerical tests of the metrics designed to accomplish this feat, this paper shows how an index proposed by Mielke can, with a minor modification, satisfy a series of desired properties, namely to be adimensional, bounded, symmetric, easy to compute and directly interpretable with respect to r. We thus show that this index can be considered as a natural extension to r that downregulates the value of r according to the bias between analysed datasets. The paper also proposes an effective way to disentangle the systematic and the unsystematic contribution to this agreement based on eigen decompositions. The use and value of the index is also illustrated on synthetic and real datasets.
JRC Directorate:Sustainable Resources

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