Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home Multivariate Data Modeling PCA PCA  Different Forms  
See also: PCA, eigenvectors, loadings and scores  
PCA  Different Forms
The principal components may be calculated by eigenanalysis of one of
three different matrices:
In order to see the effects of different scalings, take as an example the data set WORLDPOP, which contains some demographic data on all countries of the world (as of 1988). It is quite natural that the absolute numbers are important in this case, so go to the DataLab and look at the first two principal components using the three different matrices. For this data set, the standardization prior to the PCA does not make any sense and results in badly differentiated PC plots. However, keep in mind that the opposite may be true for other data sets. Another good approach worth checking is the 3D rotational display using
the first three principal components (start the PCA, then copy the scores
into the data matrix, and view the first three PCs by the command "3D Rotation")


Home Multivariate Data Modeling PCA PCA  Different Forms 
Last Update: 20121008