XLSTAT - Correlations and Similarity/Dissimilarity Matrix

Similarity and dissimilarity tool in XLSTAT

XLSTAT helps you to explain the relationships between variables in term of similarities and dissimilarities by measuring their proximity.

This tool offers a large number of proximity measurements between a series of objects whether they are in rows (usually the observations) or in columns (usually the variables).

Similarities and dissimilarities

The proximity between two objects is measured by measuring at what point they are similar (similarity) or dissimilar (dissimilarity).
The indexes offered depend on the nature of the data:

Similarities and dissimilarities for quantitative data

The similarity coefficients proposed by the calculations from the quantitative data are as follows: Cosine, Covariance (n-1), Covariance (n), Inertia, Gower coefficient, Kendall correlation coefficient, Pearson correlation coefficient, Spearman correlation coefficient.

The dissimilarity coefficients proposed by the calculations from the quantitative data are as follows:

  • Bhattacharya's distance,
  • Bray and Curtis' distance,
  • Canberra's distance,
  • Chebychev's distance,
  • Chi² distance,
  • Chi² metric,
  • Chord distance,
  • Squared chord distance,
  • Euclidian distance,
  • Geodesic distance,
  • Kendall's dissimilarity,
  • Mahalanobis distance,
  • Manhattan distance,
  • Ochiai's index,
  • Pearson's dissimilarity,
  • Spearman's dissimilarity.

Similarities and dissimilarities for binary data

The similarity and dissimilarity (per simple transformation) coefficients proposed by the calculations from the binary data are as follows:

  • Dice coefficient (also known as the Sorensen coefficient),
  • Jaccard coefficient,
  • Kulczinski coefficient,
  • Pearson Phi,
  • Ochiai coefficient,
  • Rogers & Tanimoto coefficient,
  • Sokal & Michener's coefficient (simple matching coefficient),
  • Sokal & Sneath's coefficient (1),
  • Sokal & Sneath's coefficient (2).

Similarities and dissimilarities for qualitative data

The similarity coefficients proposed by the calculations from the qualitative data are as follows: Cooccurrence, Percent agreement.

The dissimilarity indexes proposed by the calculations from the qualitative data are as follows: Percent disagreement.

XLSTAT

This analysis is available in the XLStat-Base addin for Microsoft Excel

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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