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Missing values (for example, empty cells and ??? 's) are counted as zeros. Any short columns in data are padded with zeros to the length of the longest column.
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Each of the k rows of data is an n-dimensional vector
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The n-by -n correlation matrix A is decomposed using singular value decomposition into three matrices:
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Creates two new columns named TEMP and VX. The column TEMP contains the value one, and the column VX contains the corresponding eigenvalue.
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Creates four new columns named TEMP, VX, VY, and VZ. The values in the three columns contain one eigenvector per row for the data in columns V1-V3. The value in column VZ contains the corresponding eigenvalues.
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Copyright IBM Corporation 2013. All Rights Reserved.
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