Launch the Principal Components platform by selecting Analyze > Multivariate Methods > Principal Components. Principal Component analysis is also available using the Multivariate and the Scatterplot 3D platforms.
The example described in Example of Principal Component Analysis uses all of the continuous variables from the sample data table.
Principal Components Launch Window
The Default option uses either the Row-wise, Pairwise, or REML methods:
Row-wise is used for data tables with no missing values.
Pairwise is used in the following circumstances:
REML is used otherwise.
n = number of rows
p = number of variables
X = n by p matrix of data values
When you select the Wide method, the data are standardized. To standardize a value, subtract its mean and divide by its standard deviation. Denote the n by p matrix of standardized data values by Xs. Then the covariance matrix of the standardized data is the correlation matrix of X and it is given as follows:
Using the singular value decomposition, Xs is written as UDiag(Λ)V’. This representation is used to obtain the eigenvectors and eigenvalues of Xs’Xs. The principal components, or scores, are given by .