Iconography of correlations

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In exploratory data analysis, the iconography of correlations.[1] is a method which consists in replacing a correlation matrix by a diagram where the “remarkable” correlations are represented by a solid line (positive correlation), or a dotted line (negative correlation).

This idea also appears in Gaussian graphic models used in particular in genome mapping. But the iconography of correlations is more general in that it does not make an assumption about the Gaussian distribution, or not, of the variables, and relies only on the geometric aspect of the Correlation coefficient.

Representation of the proximity of food profiles in Europe.

The first idea of the iconography of correlations dates back to 1975. Applied first to marine geochemistry, it was the subject of a thesis in 1981, and of an article in Cahiers de l'Analyse des Données in 1982.[2] After that, the application of the method in many branches of the aerospace industry [3] · [4] for about fifteen years, explains, paradoxically, the relative confidentiality in which it remained for a long time, companies not generally wishing to shout their solutions on the roofs. Since the creation in 1997 of a first company distributing software based on the iconography of correlations, and its teaching in some universities, the bibliography has grown widely, particularly in the medical [5] and astrophysical sectors (mass spectrometry[6] · [7]) · [8]

See also[]

  • The Bayesian Network is a graph in which the cause and effect relationships are "probabilized", unlike the Correlation Iconography, whose principle is "geometric".

References[]

  1. ^ "Une nouvelle approche dans le choix des régresseurs de la régression multiple en présence d'interactions et de colinéarités. M. Lesty, La Revue de Modulad, n°22, pp. 41–77, janvier 1999" (PDF) (in French).
  2. ^ "La Synthèse Géométrique des Corrélations Multidimensionnelles. M. Lesty et P. Buat-Ménard. Les Cahiers de l'Analyse des données, Vol. VII, n°3, 1982, pp. 355–370" (PDF) (in French).
  3. ^ M. Lesty et M. Coindoz. (1988) Une méthode pour la F.M.S. des bases de connaissances de système experts. Une application de CORICO. 6e Colloque International de Fiabilité et de Maintenabilité. Textes des conférences, p. 252–257 – Organisé par le Centre National d'Études Spatiales (C.N.E.S.), 3–7 octobre 1988, Strasbourg.
  4. ^ Analyse des Corrélations et Fabrication des Composites. C. Vallée et X. Le Méteil. La Maîtrise du risque dans la Construction Aéronautique. Phoebus 19 (tome 2) – Quatrième trimestre 2001
  5. ^ Geometric Method and Generalized Linear Models: Two opposite Multiparametric Approaches Illustrated on a Sample of Pituitary Adenomas. Lesty C., Pleau-Varet J. & Kujas M. Journal of Applied Statistics Vol. 31(2): p. 191–213. February 2004.
  6. ^ Multi-correlation analyses of TOF-SIMS spectra for mineralogical studies." C. Engrand, J. Lespagnol, P.Martin, L. Thirkell, R. Thomas. Applied Surface Science 231–232 (2004) 883–887
  7. ^ Chemometric evaluation of time-of-flight secondary ion mass spectrometry data of minerals in the frame of future in situ analyses of cometary material by COSIMA onboard ROSETTA." Engrand C;, Kissel J., Krueger F.R., Martin P., Silén J., Thirkel L.l, Thomas R., Varmuza K. (2006). (Rapid Communications in Mass Spectrometry Vol. 20, Issue 8 p. 1361–1368) Published Online: 23 march 2006 (www.interscience.wiley.com).
  8. ^ "Growing Medium Type Affects Organic Fertilizer Mineralization and CNPS Microbial Enzyme Activities. Louise Paillat, Patrice Cannavo, Fabrice Barraud, Lydie Huché-Thélier and René Guénon, Agronomy 2020, 10, 1955".

External links[]

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