Abstract
The multivariate statistic has been used in divergence studies concerning plants species. Analysis of the similarity or distance among individuals is an important tool for population. This among aims to present the main show the main coefficients of similarity and dissimilarity and their properties and the importance of axioms for the complement of similarity for the methods in cluster analysis. We evaluated the changes caused by five different similarity coefficients in the group of 11 plots and 17 species. We tested the coefficients of Jaccard, Sorensen-Dice, Simple Agreement, Russel e Rao e Rogers e Tanimoto comparisons being made between them by cophenetic correlations, Rand, adjusted Rand and stress between the distances obtained by the addition of these coefficients, and also by means of dendrograms (visual inspection), projection efficiency in a two-dimensional space and groups formed by the method of average linkage. The results showed that the use of different similarity coefficients caused few changes in the grouping of installments in groups, and the validation obtained between similar plots. Even though few changes in the structure of most different groups, these coefficients changed some relationships between plots with high similarity.
Highlights
Multivariate statistical techniques have been widely used in forestry studies involving climate, soil, relief and vegetation variables simultaneously
When the objective is to classify groups, a large number ofsimilarity coefficients are found in the literature Jaccard, Sorensen-Dice, Simple concordance, Russell and Rao and Rogers and Tanimoto, and it is possible to observe different coefficients used with the same or different purposes
The calculation and structure of the numerical analysis result is obtained from the association matrix, which does not necessarily reflect all the information originally contained in the data matrix, as the objects or descriptors are represented in reduced space
Summary
Multivariate statistical techniques have been widely used in forestry studies involving climate, soil, relief and vegetation variables simultaneously. . The calculation and structure of the numerical analysis result is obtained from the association matrix, which does not necessarily reflect all the information originally contained in the data matrix, as the objects or descriptors are represented in reduced space. For each variable, one of the following settings must be observed: 0-0, 0-1, 1-0 or 1-1, the first value being relative to observation i and the second to observation j The coefficients of these variables normally focus on measuring (dis)similarity, based on counting the agreements (positive or negative) that exist between the elements. The second group of distances is symmetrical These coefficients do not follow the triangle inequality axiom. Successive coefficients have been rediscovered by several authors and may be known under different names
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