Description Usage Arguments Value Examples

Transforms the data X by centring and scaling using *X_{ij}^{'} = \frac{X_{i}-μ_{i}}{σ_{i}}* where *μ_{i}* and *σ_{i}* are robust estimates for
the mean and standard deviation of each variate (column), *X_{i}*, of the multivariate time series X. The estimates are calculated using the median and median absolute deviation.
This method is the default value for the
transform argument used by the `capa`

function, since the capa method assumes that the typical distribution of the data is standard normal.

1 | ```
robustscale(X)
``` |

`X` |
A numeric matrix containing the data to be transformed. Each column corresponds to a component and each row to an observation. The time series data classes ts, xts, and zoo are also supported. |

A numeric matrix containing the transformed data.

1 2 3 4 5 6 7 8 9 10 11 | ```
library(anomaly)
# generate some multivariate data
set.seed(0)
X<-simulate(n=1000,p=4,mu=10,locations=c(200,400,600),
duration=100,proportions=c(0.25,0.5,0.75))
# compare the medians of each variate and transformed variate
head(apply(X,2,median))
head(apply(robustscale(X),2,median))
# compare the variances of each variate and transformed variate
head(apply(X,2,var))
head(apply(robustscale(X),2,var))
``` |

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