Performs robust quadratic discriminant analysis and returns
the results as an object of class QdaCov
(aka constructor).
QdaCov(x, ...)# S3 method for default
QdaCov(x, grouping, prior = proportions, tol = 1.0e-4,
method = CovControlMcd(), ...)
a matrix or data frame containing the explanatory variables (training set).
grouping variable: a factor specifying the class for each observation.
prior probabilities, default to the class proportions for the training set.
tolerance
method
arguments passed to or from other methods
Returns an S4 object of class QdaCov
Still an experimental version!
details
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1--47. URL http://www.jstatsoft.org/v32/i03/.
# NOT RUN {
## Example anorexia
library(MASS)
data(anorexia)
## start with the classical estimates
qda <- QdaClassic(Treat~., data=anorexia)
predict(qda)@classification
## try now the robust LDA with the default method (MCD with pooled whitin cov matrix)
rqda <- QdaCov(Treat~., data= anorexia)
predict(rqda)@classification
## try the other methods
QdaCov(Treat~., data= anorexia, method="sde")
QdaCov(Treat~., data= anorexia, method="M")
QdaCov(Treat~., data= anorexia, method=CovControlOgk())
# }
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