Robust mean and covariance matrix using Huber-type weight

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Description

Robust mean and covariance matrix using Huber-type weight.

Usage

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rsem.emmusig(xpattern, varphi=.1, max.it=1000, st='i')

Arguments

xpattern

Missing data pattern output from rsem.pattern.

varphi

Proportion of data to be down-weighted. Default is 0.1.

max.it

Maximum number of iterations for EM. Default is 1000

st

Starting values for EM algorithm. The default is 0 for mean and I for covariance. Alternative, the starting values can be estimated according to MCD.

Details

Estimate mean and covariance matrix using the expectation robust (ER) algorithm.

Value

err

Error code. 0: good. 1: maximum iterations are exceeded.

mu

Mean vector

sigma

Covariance matrix

Author(s)

Ke-Hai Yuan and Zhiyong Zhang

References

Ke-Hai Yuan and Zhiyong Zhang (2011) Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

See Also

rsem.emmusig

Examples

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#dset<-read.table('MardiaMV25.dat.txt', na.string='-99')  
#dset<-data.matrix(dset)                                  
#n<-dim(dset)[1]
#p<-dim(dset)[2]
#miss_pattern<-rsem.pattern(n,p,dset)
#misinfo<-miss_pattern$misinfo
#V_forana<-c(1,2,4,5)
#em_results<-rsem.emmusig(dset,misinfo)
#em_results