TRC: Transformed rank correlations for multivariate outlier...

In modi: Multivariate outlier detection and imputation for incomplete survey data

Description

TRC starts from bivariate Spearman correlations and obtains a positive definite covariance matrix by back-transforming robust univariate medians and mads of the eigenspace. TRC can cope with missing values by a regression imputation using the a robust regression on the best predictor and it takes sampling weights into account.

Usage

TRC(data, weights, overlap = 3, mincor = 0, robust.regression = "rank", 
gamma = 0.5, prob.quantile = 0.75, alpha = 0.05, md.type = "m", monitor = FALSE)

Arguments

data	a data frame or matrix with the data
weights	sampling weights
overlap	minimum number of jointly observed values for calculating the rank correlation
mincor	minimal absolute correlation to impute
robust.regression	type of regression: "irls" is iteratively reweighted least squares M-estimator, "rank" is based on the rank correlations
gamma	minimal number of jointly observed values to impute
prob.quantile	if mads are 0 try this quantile of absolute deviations
alpha	(1-alpha) Quantile of F-distribution is used for cut-off
md.type	Type of Mahalanobis distance when missing values occur: "m" marginal (default), "c" conditional
monitor	if TRUE verbose output

Details

TRC is similar to a one-step OGK estimator where the starting covariances are obtained from rank correlations and an ad hoc missing value imputation plus weighting is provided.

Value

TRC returns a list whose first component output is a sublist with the following components:

sample.size	number of observations
number.of.variables	number of variables
number.of.missing.items	number of missing values
significance.level	1-alpha
computation.time	elapsed computation time
medians	componentwise medians
mads	componentwise mads
center	location estimate
scatter	covariance estimate
robust.regression	input parameter
md.type	input parameter
cutpoint	The default threshold MD-value for the cut-off of outliers

The further components returned by TRC are:

outind	Indicator of outliers
dist	Mahalanobis distances (with missing values)

Author(s)

Beat Hulliger

References

B\'eguin, C., and Hulliger, B. (2004). Multivariate oulier detection in incomplete survey data: The epidemic algorithm and transformed rank correlations. Journal of the Royal Statistical Society, A 167(Part 2.), 275-294.

Examples

data(bushfirem, bushfire.weights)
det.res <- TRC(bushfirem, weights=bushfire.weights)
PlotMD(det.res$dist, ncol(bushfirem))
print(det.res)

modi documentation built on May 2, 2019, 6:48 p.m.