Outlier Detection Using Invariant Coordinate Selection
Selection of Nonnormal Invariant Components Using Marginal Normality T...
Selection of Nonnormal Invariant Components Using Simulations
Selection of Nonnormal Invariant Components Using Marginal Normality T...
Selection of Nonnormal Invariant Components Using Simulations
Cut-Off Values Using Simulations for the Detection of Extreme ICS Dist...
Cut-Off Values Using Simulations for the Detection of Extreme ICS Dist...
Squared ICS Distances for Invariant Coordinates
Outlier Detection Using ICS
Squared ICS Distances for Invariant Coordinates
Outlier Detection Using ICS
Class icsOut
tools:::Rd_package_title("ICSOutlier")
Distances Plot for an 'ICS_Out' Object
Distances Plot for an icsOut Object
Vector of Outlier Indicators
Vector of Outlier Indicators
Summary of an 'ICS_Out' ObjectSummarizes an 'ICS_Out' object in an inf...
Summarize a icsOut object
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.