OutlierPCDist-class function

Class "OutlierPCDist" - Outlier identification in high dimensions using using the PCDIST algorithm

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. 1.1

class

Objects from the Class

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.

Slots

• covobj:: A list containing intermediate results of the PCDIST algorithm for each class
• k:: Number of selected PC
• call, counts, grp, wt, flag, method, singularity:: from the "Outlier" class.

Extends

Class "Outlier", directly.

Methods

• getCutoff: Return the cutoff value used to identify outliers

References

A.D. Shieh and Y.S. Hung (2009). Detecting Outlier Samples in Microarray Data, Statistical Applications in Genetics and Molecular Biology Vol. 8.

Filzmoser P & Todorov V (2013). Robust tools for the imperfect world, Information Sciences 245 , 4--20. tools:::Rd_expr_doi("10.1016/j.ins.2012.10.017") .

Author(s)

Valentin Todorov valentin.todorov@chello.at

See Also

OutlierPCDist, Outlier

Examples

showClass("OutlierPCDist")