Description Usage Arguments Details Value Author(s) References See Also Examples

Computer intensive method for linear dimension reduction that minimizes the classification error directly.

1 2 3 4 5 6 7 8 9 10 | ```
meclight(x, ...)
## Default S3 method:
meclight(x, grouping, r = 1, fold = 10, ...)
## S3 method for class 'formula'
meclight(formula, data = NULL, ..., subset, na.action = na.fail)
## S3 method for class 'data.frame'
meclight(x, ...)
## S3 method for class 'matrix'
meclight(x, grouping, ..., subset, na.action = na.fail)
``` |

`x` |
(required if no formula is given as the principal argument.) A matrix or data frame containing the explanatory variables. |

`grouping` |
(required if no formula principal argument is given.) A factor specifying the class for each observation. |

`r` |
Dimension of projected subspace. |

`fold` |
Number of Bootstrap samples. |

`formula` |
A formula of the form |

`data` |
Data frame from which variables specified in formula are preferentially to be taken. |

`subset` |
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) |

`na.action` |
A function to specify the action to be taken if NAs are found.
The default action is for the procedure to fail.
An alternative is |

`...` |
Further arguments passed to |

Computer intensive method for linear dimension reduction that minimizes the classification error in the projected
subspace directly. Classification is done by `lda`

. In contrast to the reference function minimization is
done by Nelder-Mead in `optim`

.

`method.model` |
An object of class ‘lda’. |

`Proj.matrix` |
Projection matrix. |

`B.error` |
Estimated bootstrap error rate. |

`B.impro` |
Improvement in |

Maria Eveslage, Karsten Luebke, [email protected]

Roehl, M.C., Weihs, C., and Theis, W. (2002):
Direct Minimization in Multivariate Classification. *Computational Statistics*, 17, 29-46.

1 2 3 |

```
Loading required package: MASS
Dimension of projection: 1
est. bootstrap error rate: 0.01333333
est. improvement to LDA: 0.006666667
Projection matrix:
Proj.dim 1
Sepal.Length 0.8293776
Sepal.Width 1.8155191
Petal.Length -2.2012117
Petal.Width -2.8104603
```

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