minmis

Given two vectors each corresponding to a set of categories,
this function finds the minimum number of misallocations
by rotating the categories.

 cluster

We provide functions to fit finite mixtures of multivariate normal or t-distributions to
data with various factor analytic structures adopted for the covariance/scale matrices. The
factor analytic structures available include mixtures of factor analyzers and mixtures of common
factor analyzers. The latter approach is so termed because the matrix of factor loadings is
common to components before the component-specific rotation of the component factors to
make them white noise. Note that the component-factor loadings are not common after
this rotation. Maximum likelihood estimators of model parameters are obtained via the
Expectation-Maximization algorithm. See descriptions of the algorithms used in
McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8>
McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2>
McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4>
McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015>
Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149>
Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112>
McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.

Suren Rathnayake

EMMIXmfa

Mixture Models with Component-Wise Factor Analyzers

minmis function

<dl> <dt>cls</dt>
<dd>A numeric or character vector of labels.</dd> <dt>hat_cls</dt>
<dd>A numeric or character vector of labels same length as <code>cls</code>.</dd></dl>

Arguments

Minimum Number of Misallocations — minmis

<dl>

 <dt>cls</dt>
<dd>A numeric or character vector of labels.</dd>

 <dt>hat_cls</dt>
<dd>A numeric or character vector of labels same length as <code>cls</code>.</dd>

</dl>

minmis: Minimum Number of Misallocations

Description

Usage

Value

Arguments

Details

See Also

Examples