# tierney: tierney In anomaly: Detecting Anomalies in Data

## Description

Transforms the data X by centring and scaling using X_{ij}^{'} = \frac{X_{ij}-μ_{ij}}{σ_{ij}} where μ_{ij} and σ_{ij} are robust quantile based sequential estimates for the mean and standard deviation of each variate (column) X_{i} of X calculated up to time j. The estimates μ_{ij} and σ_{ij} are calculated from sequential estimates for the median and inter-quartile range developed by Tierney et al (1983). This method is the default value for the transform argument used by the scapa.uv function.

## Usage

 1 tierney(X, burnin = 10) 

## Arguments

 X A numeric matrix containing the data to be transformed. The time series data classes ts, xts, and zoo are also supported. burnin Specifies the period used to stabalise the quantile estimates. The default value is 10.

## Value

A numeric matrix containing the transformed data.

## References

\insertRef

Schruben:1983:OTI:2771114.2771123anomaly

## Examples

 1 2 3 4 5 6 library(anomaly) data(machinetemp) attach(machinetemp) plot(temperature) temperature<-tierney(temperature,burnin=4305) plot(temperature) 

anomaly documentation built on Oct. 21, 2021, 1:06 a.m.