The function implements a simple, automatic outlier detection method suitable for high dimensional data that treats each class independently and uses a statistically principled threshold for outliers. The algorithm can detect both mislabeled and abnormal samples without reference to other classes.
Objects can be created by calls of the form
new("OutlierPCDist", ...) but the
usual way of creating
OutlierPCDist objects is a call to the function
OutlierPCDist() which serves as a constructor.
A list containing intermediate results of the PCDIST algorithm for each class
Number of selected PC
Return the cutoff value used to identify outliers
Valentin Todorov email@example.com
A.D. Shieh and Y.S. Hung (2009), Detecting Outlier Samples in Microarray Data, Statistical Applications in Genetics and Molecular Biology Vol. 8.
P. Filzmoser & V. Todorov (2012), Robust tools for the imperfect world, To appear.
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