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Binary discrimination diagrams

Figure 23 shows an example of such a visualization for all bivariate LDAs using the major oxides. Of the 756 training data, not all had been analysed for all major elements. The upper right triangular part of the matrices in this figure show the number of analyses for which both elements were measured. Using the same color-code but a different scale, the lower left triangular parts of the matrices show the resubstitution errors of the 55 possible bivariate LDAs. For example, the lower left triangular matrices of Figure 23 show that only 13.5% of IABs, 15.2% of MORBs and 7.4% of OIBs were misclassified by an LDA using TiO$ _2$ and K$ _2$O. The overall resubstitution error is 12%. The upper right triangular parts of the same figure show that 229 out of 256 IABs, 230 out of 241 MORBs and 203 out of 259 OIBs were used for the construction of the LDA, accounting for a total of 662 out of 756 training data. Figure 24 shows the same thing for QDA.

Figure 25 visualizes the results of all possible bivariate LDAs for the complete dataset of 45 elements. On the whole, Ti jumps out as the apparently best overall discriminator. One might think that the Tm-Sc diagram performs very well, considering that the overall error (shown in the upper right triangle of the lower right matrix of Figure 25) is only 7.7%. 12% of the IABs, 8.8% of the OIBs and only 2.4% of the MORBs in the training data were misclassified. However, the upper right triangular matrices of the same figure show that only 101 of 756 training data were used for the classification. Only 25/256 of the IABs, 42/241 of the MORBs and 34/259 of the OIBs were analysed for both Tm and Sc, thereby greatly reducing the reliability of the classification. Figure 25 shows the results of all possible bivariate QDAs for the database of 45 elements. The strikingly different colors of the lower triangular matrices on this figure illustrate the difficulties in classifying IABs. Both MORBs and IABs are relatively easy to separate, but the geochemical variability of IABs is much larger, for reasons discussed before.


next up previous
Next: Ternary discrimination diagrams Up: An exhaustive exploration of Previous: An exhaustive exploration of
Pieter Vermeesch 2005-11-21