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

In gentok/ipbridging: Bridging Ideal Point Estimates

Description

This function is a modified version of MDmiss function in modi package.

Usage

mahalanobis_miss(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).
...	Additional arguments passed to mahalanobis if no missing values exist.

Details

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.

The function loops over the observations unless all values are non-missing. 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 (Original in modi package). Modified by Gento Kato.

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

gentok/ipbridging documentation built on March 29, 2020, 3:06 a.m.