BiplotsCRC Press, 01.12.1995 - 280 Seiten Biplots are the multivariate analog of scatter plots, approximating the multivariate distribution of a sample in a few dimensions to produce a graphic display. In addition, they superimpose representations of the variables on this display so that the relationships between the sample and the variable can be studied. Like scatter plots, biplots are useful for detecting patterns and for displaying the results found by more formal methods of analysis. In recent years the theory of biplots has been considerably extended. The approach adopted here is geometric, permitting a natural integration of well-known methods, such as components analysis, correspondence analysis, and canonical variate analysis as well as some newer and less well-known methods, such as nonlinear biplots and biadditive models. |
Inhalt
Principal components analysis PCA | 9 |
Other linear biplots | 31 |
Multiple correspondence analysis | 51 |
1 | 80 |
Introduction | 86 |
9 | 140 |
Biadditive models | 142 |
Correspondence analysis CA | 175 |
Relationships between CA and MCA | 194 |
Other topics | 210 |
Algebraic results | 233 |
31 | 242 |
References | 265 |
| 273 | |
Häufige Begriffe und Wortgruppen
algorithm axis basic points biadditive models biplot axes Burt matrix canonical categorical variables category-level centroid Chapter CLPs column-plane column-points contingency tables coordinates correlation correspondence analysis data-matrix ddistance defined diag diagonal blocks dimensions discussed Eckart-Young theorem eigenvectors embedding Equation extended matching coefficient genotypes given gives Gower hence indicator matrix inner-product interpolation interpretation intersection kth variable least-squares linear biplot Mahalanobis distances markers methods minimizes multidimensional scaling multivariate NM NM nonlinear biplots normalized origin orthogonal matrix orthogonal projection parameters planes plot prediction regions predictive biplot Procrustes Procrustes analysis pseudosamples Pythagorean Pythagorean distance quantitative variables rank reference system regression representation residual row and column row-points samples scaling SF SF shown shows singular value singular value decomposition solution space spectral decomposition subspace sum-of-squares tion trajectories transformation two-dimensional two-way table unit variant vector-sum x² distance zero