Table 4.
Summary of predictor classification performance of the linear and nonlinear discovery method and the proposed method for Model 2
| LAND | PSC | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| σ | η | L | NL | LN | Nil | CC | L | Q | BQ | Nil | CC |
| 1 | 0·0 | 3·0 | 1·6 | 2·0 | 12·5 | 47 | 3·0 | 1·0 | 3·0 | 13·0 | 99 |
| 0·5 | 3·0 | 1·1 | 2·0 | 11·8 | 16 | 2·9 | 1·0 | 3·0 | 13·0 | 89 | |
| 2 | 0·0 | 3·0 | 1·5 | 1·9 | 11·2 | 14 | 2·8 | 1·0 | 3·0 | 13·0 | 74 |
| 0·5 | 3·0 | 0·8 | 1·8 | 9·7 | 1 | 2·2 | 1·0 | 2·8 | 13·0 | 23 | |
| Average SD | 0·2 | 0·6 | 0·2 | 2·0 | 0·3 | 0·1 | 0·1 | 0·1 | |||
LAND, linear and nonlinear discovery method; PSC, the proposed polynomial structure classification method; σ, model error standard deviation; η, predictor correlation parameter; L, linear; NL, nonlinear; L-N, linear-nonlinear mixture; Nil, noise; CC, proportion (%) of models with fully correct variable classifications; Q, quadratic; BQ, beyond quadratic; Average SD, block-averaged standard deviation.