weird (version 2.1.0)

mvscale: Compute robust multivariate scaled data

Description

A multivariate version of base::scale(), that takes account of the covariance matrix of the data, and uses robust estimates of center, scale and covariance by default. The centers are removed using medians, the scale function for univariate data is s_Qn, the covariance matrix for multivariate data is estimated using a robust OGK estimate. The data are scaled using the Cholesky decomposition of the inverse (co)variance. Then the scaled data are returned. Details of the methods are provided by Hyndman (2026).

Usage

mvscale(
  object,
  center = stats::median,
  scale = robustbase::s_Qn,
  cov = robustbase::covOGK,
  warning = TRUE
)

Value

A vector, matrix or data frame of the same size and class as object, but with numerical variables replaced by scaled versions.

Arguments

object

A vector, matrix, or data frame containing some numerical data.

center

A function to compute the center of each numerical variable. Set to NULL if no centering is required.

scale

A function to scale each numerical variable. When cov = robustbase::covOGK(), it is passed as the sigmamu argument.

cov

A function to compute the covariance matrix. Set to NULL if no rotation required.

warning

Should a warning be issued if non-numeric columns are ignored?

Author

Rob J Hyndman

Details

Optionally, the centering and scaling can be done for each variable separately, so there is no rotation of the data, by setting cov = NULL. Also optionally, non-robust methods can be used by specifying center = mean, scale = stats::sd(), and cov = stats::cov(). Any non-numeric columns are retained with a warning.

References

Hyndman, R J (2026) "That's weird: Anomaly detection using R", Section 2.6, 3.6 and 3.7.

See Also

base::scale(), stats::sd(), stats::cov(), robustbase::covOGK(), robustbase::s_Qn()

Examples

# Univariate z-scores (no rotation)
z <- mvscale(faithful, center = mean, scale = sd, cov = NULL, warning = FALSE)
# Non-robust scaling with rotation
z <- mvscale(faithful, center = mean, cov = stats::cov, warning = FALSE)
# Robust scaling and rotation
z <- mvscale(faithful, warning = FALSE)