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

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

#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

Example output

Loading required package: MASS
Loading required package: lavaan
This is lavaan 0.6-3
lavaan is BETA software! Please report any bugs.

rsem documentation built on May 2, 2019, 4:16 p.m.