GIMCD: Gaussian imputation followed by MCD

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

Gaussian imputation followed by MCD

Description

Gaussian imputation uses the classical non-robust mean and covariance estimator and then imputes predictions under the multivariate normal model. Outliers may be created by this procedure. Then a high-breakdown robust estimate of the location and scatter with the Minimum Covariance Determinant algorithm is obtained and finally outliers are determined based on Mahalanobis distances based on the robust location and scatter.

Usage

GIMCD(data, alpha = 0.05, seedem = 23456789, seedmcd)

Arguments

data	a data frame or matrix with the data.
alpha	a threshold value for the cut-off for the outlier Mahalanobis distances.
seedem	random number generator seed for EM algorithm
seedmcd	random number generator seed for MCD algorithm, if seedmcd is missing, an internal seed will be used.

Details

Normal imputation from package norm and MCD from package MASS. Note that currently MCD does not accept weights.

Value

Result is stored in a global list GIMCD.r:

center

robust center

scatter

robust covariance

alpha

quantile for cut-off value

computation.time

elapsed computation time

outind

logical vector of outlier indicators

dist

Mahalanobis distances

Author(s)

Beat Hulliger

References

Béguin, C. and Hulliger, B. (2008), The BACON-EEM Algorithm for Multivariate Outlier Detection, in Incomplete Survey Data, Survey Methodology, Vol. 34, No. 1, pp. 91-103.

See Also

cov.rob

Examples

data(bushfirem)
det.res <- GIMCD(bushfirem, alpha = 0.1)
print(det.res$center)
PlotMD(det.res$dist, ncol(bushfirem))

martinSter/modi documentation built on March 14, 2023, 12:09 p.m.