Biplots: qualititative data

Gower, John C.; Le Roux, Niël J. and Gardner-Lubbe, Sugnet (2016). Biplots: qualititative data. Wiley Interdisciplinary Reviews: Computational Statistics, 8(2) pp. 82–111.

DOI: https://doi.org/10.1002/wics.1377

Abstract

A previous paper, Biplots: Quantitative data, dealt exclusively with biplots for quantitative data. This paper is mainly concerned with qualitative data or data in the form of counts. Qualitative data can be nominal or ordinal, and it is usually reported in a coded numerical form. In the analysis of qualitative data, many methods can be grouped as quantification methods (e.g., categorical principal component analysis, correspondence analysis, multiple correspondence analysis, homogeneity analysis): transforming qualities into quantitative values that may then be treated with quantitative methods. All the features of quantitative biplots are found in qualitative biplots, but calibrated interpolation axes become labeled category-level points and calibrated prediction axes become prediction regions. Interpretation remains in terms of distance, inner products, and sometimes area

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