PrincipalComponentAnalysis Class

Principal component analysis (PCA) is a form of factor analysis or dimension reduction. It attempts to identify a small number of factors which allow most of the variation in the data to be explained by giving the vales for the factor dimensions.

Mathematically, PCA constructs an alternative set of orthonormal basis vectors for a multivariate data set. These basis vectors, called the principal components, are ordered by the total variance explained by each.

Suppose, for example, you measure the value of different possessions possessions for a sample of people: home value, car value, furniture value, etc. You might expect that much of the variation in these numbers can be explained by one underlying factor, which you might call "richness". If this is true, then a PCA analysis will show that the most principal component explains a very large faction of the total variance, and the other less principal components will explain only small fractions of the total variance.

Note that PCA is not invariant with respect to the re-scaling of individual variables.

Note that PCA is an exploratory technique, not a hypothesis test.

To carry out a principal component analysis, call the PrincipalComponentAnalysis method.

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