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

`meclight(x, ...)`# S3 method for default
meclight(x, grouping, r = 1, fold = 10, ...)
# S3 method for formula
meclight(formula, data = NULL, ..., subset, na.action = na.fail)
# S3 method for data.frame
meclight(x, ...)
# S3 method for 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 `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`

.

An object of class ‘lda’.

Projection matrix.

Estimated bootstrap error rate.

Improvement in `lda`

error rate.

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`

.

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

# NOT RUN { data(iris) meclight.obj <- meclight(Species ~ ., data = iris) meclight.obj # }