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)
An object of class ‘lda’.
Projection matrix.
Estimated bootstrap error rate.
Improvement in lda error rate.
(required if no formula is given as the principal argument.) A matrix or data frame containing the explanatory variables.
(required if no formula principal argument is given.) A factor specifying the class for each observation.
Dimension of projected subspace.
Number of Bootstrap samples.
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 frame from which variables specified in formula are preferentially to be taken.
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
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.
Maria Eveslage, Karsten Luebke, karsten.luebke@fom.de
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.
predict.meclight
data(iris)
meclight.obj <- meclight(Species ~ ., data = iris)
meclight.obj
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