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

Share:

Description

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 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

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

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.

See Also

OutlierPCDist, Outlier

Examples

1
showClass("OutlierPCDist")

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.