Weighted K-means via Lloyd's algorithm

A general implementation of Structural Equation Models
with latent variables (MLE, 2SLS, and composite likelihood
estimators) with both continuous, censored, and ordinal
outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
Mixture latent variable models and non-linear latent variable models
(Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>).
The package also provides methods for graph exploration (d-separation,
back-door criterion), simulation of general non-linear latent variable
models, and estimation of influence functions for a broad range of
statistical models.

Klaus Holst

lava

Latent Variable Models

Klaus K. Holst

Brice Ozenne

Thomas Gerds

wkm function

<dl><dt>x</dt>
<dd>Data (or formula)</dd>
<dt>mu</dt>
<dd>Initial centers (or number centers chosen randomly among x)</dd>
<dt>data</dt>
<dd>optional data frmae</dd>
<dt>weights</dt>
<dd>Optional weights</dd>
<dt>iter.max</dt>
<dd>Max number of iterations</dd>
<dt>n.start</dt>
<dd>Number of restarts</dd>
<dt>init</dt>
<dd>method to create initial centres (default kmeans++)</dd>
<dt>...</dt>
<dd>Additional arguments to lower level functions</dd></dl>

Arguments

Author

Weighted K-means — wkm

<dl>

<dt>x</dt>
<dd>Data (or formula)</dd>


<dt>mu</dt>
<dd>Initial centers (or number centers chosen randomly among x)</dd>


<dt>data</dt>
<dd>optional data frmae</dd>


<dt>weights</dt>
<dd>Optional weights</dd>


<dt>iter.max</dt>
<dd>Max number of iterations</dd>


<dt>n.start</dt>
<dd>Number of restarts</dd>


<dt>init</dt>
<dd>method to create initial centres (default kmeans++)</dd>


<dt>...</dt>
<dd>Additional arguments to lower level functions</dd>

</dl>

wkm: Weighted K-means

Description

Usage

Arguments

Author