meclight.default {klaR} | R Documentation |
Computer intensive method for linear dimension reduction that minimizes the classification error directly.
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 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 . |
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 lda error rate. |
Maria Eveslage, Karsten Luebke
Roehl, M.C., Weihs, C., and Theis, W. (2002): Direct Minimization in Multivariate Classification. Computational Statistics, 17, 29-46.
data(iris) meclight.obj <- meclight(Species ~ ., data = iris) meclight.obj