# rsem.emmusig: Robust mean and covariance matrix using Huber-type weight In rsem: Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

## Description

Robust mean and covariance matrix using Huber-type weight.

## Usage

 `1` ```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

`rsem.emmusig`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```#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 ```

rsem documentation built on May 31, 2017, 1:32 a.m.