TRC
starts from bivariate Spearman correlations and obtains a positive definite covariance matrix by
backtransforming 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.
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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 Mestimator, "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 

md.type 
Type of Mahalanobis distance when missing values occur: "m" marginal (default), "c" conditional 
monitor 
if 
TRC is similar to a onestep OGK estimator where the starting covariances are obtained from rank correlations and an ad hoc missing value imputation plus weighting is provided.
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 

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 MDvalue for the cutoff of outliers 
The further components returned by TRC
are:
outind 
Indicator of outliers 
dist 
Mahalanobis distances (with missing values) 
Beat Hulliger
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.), 275294.
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