No more extreme samples are observed in the scores. This is called the principal axis property: Having knowledge on the quality of the data can help in assessing the number of components. Note, that the high correlation is apparently governed by an extreme sample — a potential outlier which will be discussed later.
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There is a fundamental conceptual difference between the two approaches, which is important to understand. In this case, R components are considered, but fewer components extreme anal iii also be considered by adjusting the summation in eqn Hence, throughout this paper, the magnitude of variation will simply be expressed in terms of sums of squares. That is, the plot must be shown as original scores where the basis is the loading vector.
Visualizing and interpreting loadings. Data fusion concerns integrating functional genomics data and fusing data with prior biological knowledge. Whenever in doubt as to whether to remove an outlier or not, it is often instructive to compare the models before and after removal.