Multivariate Tools in gOcad

@inproceedings{LafferièreRM2000,
 abstract = { As reducing the dimensionality of multi-component datasets is a frequent problem in geosciences, multivariate methods have been implemented in C++ in gOcad. The toolbox comprises Ordinary Principal Component Analysis, a popular methods used for reducing redundancy in datasets into a few linear combinations (factors), and Generalized Principal Component Analysis Tool (PCA or GPCA), a sort of generalized canonical method. These multivariate methods can be performed on any gOcad object on which properties have been defined (Vset, Tsurf , Voxet, Sgrid, Wells). The resulting factors (factor scores) are stored as property vectors. Other results (correlation matrix, percentage of explained inertia, factor loading) are saved as external ASCII files. Correlation diagrams are visualized in a 2D or 3D camera allowing better interpretation of results. A number of practical advanced methods have been also included such as robust estimates of statistical parameters (means, standard deviation, correlation matrix) and rotation of the resulting factors using varimax and contraminimax algorithms. },
 author = { Lafferrière, F. AND Royer, Jean-Jacques AND Mallet, Jean-Laurent },
 booktitle = { 20th gOcad Meeting },
 month = { "june" },
 publisher = { ASGA },
 title = { Multivariate Tools in gOcad },
 year = { 2000 }
}