TIBCO Statistica® Principal Components & Classification Analysis
Data are often collected on variables that are not only correlated, but also are large in number. This makes the interpretation of the data and the detection of its structure difficult. The PCCA module has two goals:
- Reduction in the number of variables to a smaller number of, ‘representative' and ‘uncorrelated' variables (i.e. dimension reduction)
- Classification of variables and rows of data.
The methods used in the Principal Components & Classification Analysis (PCCA) module are similar to those offered in the Factor Analysis module, but differs in the following ways:
- PCCA does not use any iterative methods to extract factors
- PCCA allows you to consider some variables and/or cases as supplementary
- PCCA allows you to analyze the data collected on variables that are heterogeneous with respect to their means or with respect to both their means and standard deviations, by providing an option to analyze covariance matrices as well as correlation matrices.
See Jambu, 1991 for additional details on formulas used for PCA.