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Biplots: qualititative data
Author(s) -
Gower John C.,
Le Roux Niël J.,
GardnerLubbe Sugnet
Publication year - 2016
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1377
Subject(s) - biplot , categorical variable , principal component analysis , exploratory data analysis , correspondence analysis , statistical analysis , functional data analysis , quantitative analysis (chemistry) , data mining , qualitative analysis , multivariate analysis , varimax rotation , ordinal data , multivariate statistics , statistics , homogeneity (statistics) , statistical graphics , computer science , mathematics , interpretation (philosophy) , qualitative research , chemistry , social science , biochemistry , graphics , cronbach's alpha , computer graphics (images) , descriptive statistics , programming language , chromatography , sociology , genotype , gene
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. WIREs Comput Stat 2016, 8:82–111. doi: 10.1002/wics.1377 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data