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

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Different variables in a PCA will have diering contributions to a principal<br />

component axis. These contributions are described in terms of numerical<br />

loadings, which can be either positive or negative. The absolute value of the<br />

loading is directly proportional to the variable's contribution to the principal<br />

component. If the loadings of neariy al1 or al1 variables are positive for that<br />

principal component, the component describes size variation for the sample. If<br />

many loadings are negative, the principal component probably describes shape<br />

variation (Pimente1 1979). Careful examination of loadings can detemine the<br />

type of shape variation that a given principal wmponent reveals.<br />

The relative importance of a variable to a principal component is determined<br />

by its correlation with that component. A correlation is simply the correlation<br />

coefficient between a variable and its Ioading on the PC. A high absolute value<br />

for a correlation indicates a strong association between the loading and the<br />

original variable. If the correlation is low, the variable is less important in<br />

explaining the varianœ along that PC. Regardless of the relative importance of a<br />

variable to a PC, the sign of the mrrelation indicates a shape or size contribution<br />

congruently with the sign of the variable's loading. For example, if a variable has<br />

both a negative correlation and a negative loading, its importance for shape<br />

variation if strongly validated.<br />

PCA therefore determines which Principal Components are responsible for<br />

given amounts of variation (Pimentel 1979). Principal Component 1 or PC1 (the<br />

largest eigenvector), is responsible for the most variation, PC2 for the second

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