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THE UNIVERSITY OF CALGARY Eric Snively A ... - Ohio University

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APPENDIX Principal Components Analysis<br />

The mechanics and theory behind morphological Principal Components<br />

Analysis (PCA) are relatively simple. Dimensions between landmarks on an<br />

anatomical structure, or angles subtended by its contours, are measured for a<br />

number of specimens. These data are typically log transforrned and entered into<br />

a matrix of measurements versus specimens.<br />

The variables for these rneasurements represent coordinate axes in<br />

multidimensional space. Each specimen is plotted in a given position in a<br />

multidimensional cloud of points (Sokal and Rohlf 1995). PCA determines the<br />

variance and covariance of measured dimensions. Important elements of<br />

variance and covariance describe the shape of this cloud of points, much like the<br />

length and width axes that might describe the shape of an elipse. These<br />

principal axes that represent important aspects of variation are called<br />

eigenvectors, and values which denote variance along the axes are called<br />

eigenvalues (Sokal and Rohlf 1995).<br />

Eigenvalues reveal the relative contribution of principal axes to overall<br />

variation. These important contributing factors of variation are called Principal<br />

Components (abbreviated as PC). Because diierent eigenvalues can represent<br />

either variance or covariance, they can reveal the importance size or shape.<br />

Measures of linear size might be responsible for the most variation, or a measure<br />

of shape, such as ratios of variables, might contribute more.<br />

To reveal what kind of variation a principal component represents, we must<br />

examine the impact that individuai variables have on that principal component.

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