R: Minimal Error Classification

meclight.default {klaR} | R Documentation

## Minimal Error Classification

### Description

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

### Usage

    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)

### Arguments

`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 `groups ~ x1 + x2 + ...`. That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.
`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 `na.omit`, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)
`...` | Further arguments passed to `lda`.

### Details

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`.

### Value

`method.model` | An object of class ‘lda’.
`Proj.matrix` | Projection matrix.
`B.error` | Estimated bootstrap error rate.
`B.impro` | Improvement in `lda` error rate.

### Author(s)

Maria Eveslage, Karsten Luebke, karsten.luebke@fom.de

### References

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

### See Also

`predict.meclight`

### Examples

    data(iris)
    meclight.obj <- meclight(Species ~ ., data = iris)
    meclight.obj

[Package klaR version 0.6-12 Index]