Editing Principal component analysis (section)

== Qualitative variables ==
In PCA, it is common that we want to introduce qualitative variables as supplementary elements. For example, many quantitative variables have been measured on plants. For these plants, some qualitative variables are available as, for example, the species to which the plant belongs. These data were subjected to PCA for quantitative variables. When analyzing the results, it is natural to connect the principal components to the qualitative variable ''species''.
For this, the following results are produced.
* Identification, on the factorial planes, of the different species, for example, using different colors.
* Representation, on the factorial planes, of the centers of gravity of plants belonging to the same species.
* For each center of gravity and each axis, p-value to judge the significance of the difference between the center of gravity and origin.

These results are what is called ''introducing a qualitative variable as supplementary element''. This procedure is detailed in and Husson, Lê, & Pagès (2009) and Pagès (2013).
Few software offer this option in an "automatic" way. This is the case of [http://www.coheris.com/produits/analytics/logiciel-data-mining/ SPAD] that historically, following the work of [[Ludovic Lebart]], was the first to propose this option, and the R package [http://factominer.free.fr/ FactoMineR].