CovEM: Gaussian MLE of mean and covariance

In GSE: Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data

Gaussian MLE of mean and covariance

Description

Computes the Gaussian MLE via EM-algorithm for missing data.

Usage

CovEM(x, tol=0.001, maxiter=1000)

Arguments

x	a matrix or data frame. May contain missing values, but cannot contain columns with completely missing entries.
tol	tolerance level for the maximum relative change of the estimates. Default is 0.001.
maxiter	maximum iteration for the EM algorithm. Default is 1000.

Value

An S4 object of class CovRobMiss-class. The output S4 object contains the following slots:

mu	Estimated location. Can be accessed via getLocation.
S	Estimated scatter matrix. Can be accessed via getScatter.
pmd	Squared partial Mahalanobis distances. Can be accessed via getDist.
pmd.adj	Adjusted squared partial Mahalanobis distances. Can be accessed via getDistAdj.
pu	Dimension of the observed entries for each case. Can be accessed via getDim.
call	Object of class "language". Not meant to be accessed.
x	Input data matrix. Not meant to be accessed.
p	Column dimension of input data matrix. Not meant to be accessed.
estimator	Character string of the name of the estimator used. Not meant to be accessed.

Author(s)

Mike Danilov, Andy Leung [e-mail]

GSE documentation built on Dec. 28, 2022, 1:31 a.m.