Robust Quadratic Discriminant Analysis
Performs robust quadratic discriminant analysis and returns
the results as an object of class QdaCov (aka constructor).
QdaCov(x, ...)
## Default S3 method:
QdaCov(x, grouping, prior = proportions, tol = 1.0e-4,
method = CovControlMcd(), ...)x |
a matrix or data frame containing the explanatory variables (training set). |
grouping |
grouping variable: a factor specifying the class for each observation. |
prior |
prior probabilities, default to the class proportions for the training set. |
tol |
tolerance |
method |
method |
... |
arguments passed to or from other methods |
details
Returns an S4 object of class QdaCov
Still an experimental version!
Valentin Todorov valentin.todorov@chello.at
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/.
## 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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