MDmiss: Mahalanobis distance (MD) for data with missing values

In modi: Multivariate Outlier Detection and Imputation for Incomplete Survey Data

Mahalanobis distance (MD) for data with missing values

Description

For each observation the missing dimensions are omitted before calculating the MD. The MD contains a correction factor p/q to account for the number of observed values, where p is the number of variables and q is the number of observed dimensions for the particular observation.

Usage

MDmiss(data, center, cov)

Arguments

data	the data as a dataframe or matrix.
center	the center to be used (may not contain missing values).
cov	the covariance to be used (may not contain missing values).

Details

The function loops over the observations. This is not optimal if only a few missingness patterns occur. If no missing values occur the function returns the Mahalanobis distance.

Value

The function returns a vector of the (squared) Mahalanobis distances.

Author(s)

Beat Hulliger

References

Béguin, C., and Hulliger, B. (2004). Multivariate outlier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A167 (Part 2.), pp. 275-294.

See Also

mahalanobis

Examples

data(bushfirem, bushfire)
MDmiss(bushfirem, apply(bushfire, 2, mean), var(bushfire))

modi documentation built on March 31, 2023, 8:35 p.m.