By G. Dunn

Students of mathematical biology detect smooth equipment of taxonomy with this article, which introduces taxonomic characters, the size of similarity, and the research of imperative elements. different subject matters comprise multidimensional scaling, cluster research, id and task ideas, extra. A familiarity with matrix algebra and effortless information are the only real prerequisites.

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Additional resources for An Introduction to Mathematical Taxonomy

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50 Principal components analysis The second principal component is that linear combination Y2 = a21 x 1 + a 22 x 2 + ... e. • ,a p are the latent vectors of the covariance matrix of the original characters, and that when these are normalized or scaled so that the sum of their squares is unity, the latent roots of this matrix, AI' A2 , • • • , A. p, are interpretable as the sampling variances of Yl' ... ,Yp, respectively (see D . F . Morrison, 1967). 1 + A2 + ... + Ap = Sl1 + S22 + ... + spp where sjj> i = 1, ...

One intuitively sensible answer to this question would be to choose the axis which maximizes the variance of the projections of the four points onto itself, since this will provide the maximum discrimination between the four buttercups. It is easy to show that such an axis is given by the line of best fit, in the least-squares sense, to the points; that is, the line that minimizes the sum of squares of the distances between the points and itself. 1 for the buttercup data. 5 Note that the values for the first principal component are all positive and are clearly related to the size of the buttercup flowers.

This approach was criticized by Adanson in the eighteenth century, and again in the twentieth century by numerical taxonomists such as Sneath and Sokal, because it presupposes a knowledge of the classification one wants to produce before the analysis of the data. Most numerical taxonomists argue for an equal a priori weighting of characters, although a form of 'statistical weighting' of characters is sometimes considered acceptable (see Chapter 6). However, as was seen in the preceding section, the taxonomist using numerical methods needs to be aware that different approaches to coding character states, and the use of a particular similarity or distance measure (see Chapter 3), often imply subtle differences in character weighting.