Implements K-means clustering and afterwards factorial reduction in a sequential fashion.
fit.twcfta(model, X_i_jk, full_tensor_shape, reduced_tensor_shape)# S4 method for tandem
fit.twcfta(model, X_i_jk, full_tensor_shape, reduced_tensor_shape)
Output attributes accessible via the '@' operator.
U_i_g0 - Initial object membership function matrix.
B_j_q0 - Initial factor/component matrix for the variables.
C_k_r0 - Initial factor/component matrix for the occasions.
U_i_g - Final/updated object membership function matrix.
B_j_q - Final/updated factor/component matrix for the variables.
C_k_r - Final/updated factor/component matrix for the occasions.
Y_g_qr - Derived centroids in the reduced space (data matrix).
X_i_jk_scaled - Standardized dataset matrix.
BestTimeElapsed - Execution time for the best iterate.
BestLoop - Loop that obtained the best iterate.
BestKmIteration - Number of iteration until best iterate for the K-means.
BestFaIteration - Number of iteration until best iterate for the FA.
FaConverged - Flag to check if algorithm converged for the K-means.
KmConverged - Flag to check if algorithm converged for the Factor Decomposition.
nKmConverges - Number of loops that converged for the K-means.
nFaConverges - Number of loops that converged for the Factor decomposition.
TSS_full - Total deviance in the full-space.
BSS_full - Between deviance in the reduced-space.
RSS_full - Residual deviance in the reduced-space.
PF_full - PseudoF in the full-space.
TSS_reduced - Total deviance in the reduced-space.
BSS_reduced - Between deviance in the reduced-space.
RSS_reduced - Residual deviance in the reduced-space.
PF_reduced - PseudoF in the reduced-space.
PF - Actual PseudoF value to obtain best loop.
Labels - Object cluster assignments.
FsKM - Objective function values for the KM best iterate.
FsFA - Objective function values for the FA best iterate.
Enorm - Average l2 norm of the residual norm.
Initialized tandem model.
Matricized tensor along mode-1 (I objects).
Dimensions of the tensor in full space.
Dimensions of tensor in the reduced space.
The procedure requires sequential clustering and factorial decomposition.
The K-means clustering algorithm is initially applied to the matricized tensor X_i_jk to obtain the centroids matrix X_g_jk and the membership matrix U_i_g.
The Tucker2 decomposition technique is then implemented on the centroids matrix X_g_jk to yield the core centroids matrix Y_g_qr and the component weights matrices B_j_q and C_k_r.
tandemModelssimuclustfactor tucker1966simuclustfactor
fit.twfcta tandem
X_i_jk = generate_dataset()$X_i_jk
model = tandem()
twcfta = fit.twcfta(model, X_i_jk, c(8,5,4), c(3,3,2))
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