apseudoF

Calculates and plots the CH index for k = 2, ..., maxK. The function provides an interval wide (2tol*pF) so that the choice of K is less conservative. Instead of just choosing the maximum pF, if it exists, picks the value such that its upper bound is larger than max pF.

Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" <doi:10.1007/978-3-642-51175-2_24> ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6> ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" <doi:10.1016/S0167-9473(00)00064-5> ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" <doi:10.1016/j.csda.2008.05.028> ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" <doi:10.1007/s11634-016-0263-9>.

Ionel Prunila

drclust

Simultaneous Clustering and (or) Dimensionality Reduction

Maurizio Vichi

apseudoF function

<dl><dt>data</dt>
<dd>Units x variables numeric data matrix.</dd>
<dt>maxK</dt>
<dd>Maximum number of clusters for the units to be tested.</dd>
<dt>tol</dt>
<dd>Approximation value. It is half of the length of theinterval put for each pF. 0 &lt;= tol &lt; 1. Its default value is 0.05.</dd>
<dt>model</dt>
<dd>Partitioning Models to run for each value of k. (1 = doublekm; 2 = redkm; 3 = factkm; 4 = dpcakm)</dd>
<dt>Q</dt>
<dd>Number of principal components w.r.t. variables selected for the maxK -1 partitions to be tested.</dd></dl>

Arguments

Author

pseudoF (pF or Calinski-Harabsz) index for choosing k in partitioning models — apseudoF

<dl>

<dt>data</dt>
<dd>Units x variables numeric data matrix.</dd>


<dt>maxK</dt>
<dd>Maximum number of clusters for the units to be tested.</dd>


<dt>tol</dt>
<dd>Approximation value. It is half of the length of theinterval put for each pF. 0 &lt;= tol &lt; 1. Its default value is 0.05.</dd>


<dt>model</dt>
<dd>Partitioning Models to run for each value of k. (1 = doublekm; 2 = redkm; 3 = factkm; 4 = dpcakm)</dd>


<dt>Q</dt>
<dd>Number of principal components w.r.t. variables selected for the maxK -1 partitions to be tested.</dd>

</dl>

apseudoF: pseudoF (pF or Calinski-Harabsz) index for choosing k in partitioning models

Description

Usage

Value

Arguments

Author

References

Examples