2026-04-18T06:30:32Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/326372025-12-25T01:08:08Zcom_2072_6col_2072_452953
RECERCAT
author
Greenacre, Michael
2017-07-26T10:50:51Z2017-07-26T10:50:51Z2016-08-012017-07-23T02:18:00Z
Compositional data are nonnegative data with the property of closure: that is, each set
of values on their components, or so-called parts, has a fixed sum, usually 1 or 100%.
Compositional data cannot be analyzed by conventional statistical methods, since the value of
any part depends on the choice of the other parts of the composition of interest. For example,
reporting the mean and standard deviation of a specific part makes no sense, neither does the
correlation between two parts. I propose that a small set of ratios of parts can be determined,
either by expert choice or by automatic selection, which effectively replaces the compositional
data set. This set can be determined to explain 100% of the variance in the compositional data,
or as close to 100% as required. These part ratios can then be validly summarized and analyzed
by conventional univariate methods, as well as multivariate methods, where the ratios are
preferably log-transformed.
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compositional data
logarithmic transformation
log-ratio analysis
multivariate analysis
ratios
univariate statistics.
Selection and statistical analysis of compositional ratios
info:eu-repo/semantics/workingPaper