pseudof.reduced

Computes the PseudoF score in the reduced space.

Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.

Prosper Ablordeppey

simuclustfactor

Simultaneous Clustering and Factorial Decomposition of Three-Way
Datasets

Adelaide Freitas

Giorgia Zaccaria

pseudof.reduced function

<dl><dt>bss</dt>
<dd>Between sums of squared deviations between clusters.</dd>
<dt>wss</dt>
<dd>Within sums of squared deviations within clusters.</dd>
<dt>full_tensor_shape</dt>
<dd>Dimensions of the tensor in the original space.</dd>
<dt>reduced_tensor_shape</dt>
<dd>Dimension of the tensor in the reduced space.</dd></dl>

Arguments

PseudoF Score in the Reduced-Space — pseudof.reduced

<dl>

<dt>bss</dt>
<dd>Between sums of squared deviations between clusters.</dd>


<dt>wss</dt>
<dd>Within sums of squared deviations within clusters.</dd>


<dt>full_tensor_shape</dt>
<dd>Dimensions of the tensor in the original space.</dd>


<dt>reduced_tensor_shape</dt>
<dd>Dimension of the tensor in the reduced space.</dd>

</dl>

pseudof.reduced: PseudoF Score in the Reduced-Space

Description

Usage

Value

Arguments

References

Examples