ICSOutlier

Description

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.

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Running examples in ‘ICSOutlier-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ICS_outlier
> ### Title: Outlier Detection Using ICS
> ### Aliases: ICS_outlier
> 
> ### ** Examples
> 
> # ReliabilityData example: the observations 414 and 512 are suspected to be outliers  
> library(REPPlab)
Error in library(REPPlab) : there is no package called ‘REPPlab’
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
     user system elapsed
HTP 3.852  0.183   6.298

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Running examples in ‘ICSOutlier-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: ICS_outlier
> ### Title: Outlier Detection Using ICS
> ### Aliases: ICS_outlier
> 
> ### ** Examples
> 
> # ReliabilityData example: the observations 414 and 512 are suspected to be outliers  
> library(REPPlab)
Error in library(REPPlab) : there is no package called ‘REPPlab’
Execution halted
Examples with CPU (user + system) or elapsed time > 5s

Reverse Dependencies (3)

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