EMControl

Number of replications (different randomly generated starting points) of the EM algorithm.

nrep

Maximum number of iterations in each replication of the EM algorithm.

maxiter

Numeric tolerance for testing the convergence of the EM algorithm. Convergence is assumed when the log-likelihood changes less than convtol.

convtol

Numeric tolerance for the mixture proportions. Proportions below protol, considered to be zero, are not allowed.

protol

Starting value for random generator.

seed

This function will create a control object of class <code><a href="/link/?package=MAINT.Data&version=2.3.1" emcontrol'="" data-mini-rdoc="MAINT.Data::"></a>'>EMControl</code> containing the control parameters 
 for the EM algorithm used in estimation of Gaussian mixtures by function <code><a rd-options="" href="/link/Idtmclust?package=MAINT.Data&version=2.3.1" data-mini-rdoc="MAINT.Data::Idtmclust">Idtmclust</a></code>.

Implements methodologies for modelling interval data by Normal
and Skew-Normal distributions, considering appropriate parameterizations of
the variance-covariance matrix that takes into account the intrinsic nature of
interval data, and lead to four different possible configuration structures.
The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
parameters may be estimated by maximum likelihood or robust trimmed maximum
likelihood methods.

Antonio Duarte Silva

MAINT.Data

Model and Analyse Interval Data

EMControl function

This function will create a control object of class <code><a href='<a href='EMControl'></a>'>EMControl</a></code> containing the control parameters 
 for the EM algorithm used in estimation of Gaussian mixtures by function <code><a rd-options='' href='Idtmclust'>Idtmclust</a></code>.

EMControl: Constructor function for objects of class EMControl

Description

Usage

Arguments

Value

See Also