![]() ![]() For point-counting data, classical MDS analysis of chi-square distances is shown to be equivalent to Correspondence Analysis (CA). The resulting MDS configurations can be augmented with compositional information as biplots. For compositional data, classical MDS analysis of logratio data is shown to be equivalent to Principal Component Analysis (PCA). Multidimensional Scaling (MDS) is a generally applicable method that displays the salient dissimilarities and differences between multiple samples as a configuration of points in which similar samples plot close together and dissimilar samples plot far apart. This paper reviews a number of multivariate ordination techniques to aid the interpretation of such studies. But this no longer works for larger and more complex datasets. Finally, distributional data can be compared using the Kolmogorov-Smirnov and related statistics.įor small datasets using a single provenance proxy, data interpretation can sometimes be done by visual inspection of ternary diagrams or age spectra. Point-counting data may be compared using the chi-square distance, which deals better with missing components (zero values) than the logratio distance does. For compositional data, this is best done using a logratio distance. Central to any such treatment is the ability to quantify the ‘dissimilarity’ between two samples. Each of these three data types requires separate statistical treatment. These define three distinct data types: (1) compositional data such as chemical concentrations (2) point-counting data such as heavy mineral compositions and (3) distributional data such as zircon U-Pb age spectra. The provenance of siliclastic sediment may be traced using a wide variety of chemical, mineralogical and isotopic proxies. ![]()
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January 2023
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