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. 2020 Aug 28;18:105. doi: 10.1186/s12915-020-00835-y

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Histogram of the percent of total variance each MCA dimension is able to explain. MCA recovers the total of eight dimensions to explain the total variance contained in the dataset. However, to be able to visually interpret the results, we must reduce the number of dimensions. An interpretable graphic representation of MCA results best operates in two dimensions. Therefore, we relied on the first two MCA dimensions for investigating variable relationships. Those two dimensions together explain 52.9% of the total variance (inertia) within the data while the 47.1% of the variance is lost