rsem.emmusig: Robust mean and covariance matrix using Huber-type weight

In alpha: Robust Cronbach's alpha with missing and non-normal data

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
weight	weight used in robust mean and covariance estimation.

Author(s)

Zhiyong Zhang and Ke-Hai Yuan

References

Yuan, K.-H., & Zhang, Z. (2012). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika, 77(4), 803-826.

See Also

rsem.emmusig

alpha documentation built on May 2, 2019, 6:11 p.m.