kaiserCrit

Selects the optimal number of principal components to be extracted from a dataset based on Kaiser's criterion

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

kaiserCrit function

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

Arguments

Author

Selecting the number of principal components to be extracted from a dataset — kaiserCrit

<dl>

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

</dl>

kaiserCrit: Selecting the number of principal components to be extracted from a dataset

Description

Usage

Value

Arguments

Author

References

Examples