fit.t3clus | R Documentation |
Implements simultaneous version of TWCFTA
fit.t3clus(model, X_i_jk, full_tensor_shape, reduced_tensor_shape) ## S4 method for signature 'simultaneous' fit.t3clus(model, X_i_jk, full_tensor_shape, reduced_tensor_shape)
model |
Initialized simultaneous model. |
X_i_jk |
Matricized tensor along mode-1 (I objects). |
full_tensor_shape |
Dimensions of the tensor in full-space. |
reduced_tensor_shape |
Dimensions of tensor in the reduced-space. |
The procedure performs simultaneously the sequential TWCFTA model. The model finds B_j_q and C_k_r such that the between-clusters deviance of the component scores is maximized.
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
BestIteration - Iteration yielding the best results
Converged - Flag to check if algorithm converged for the K-means
nConverges - Number of loops that converged for the K-means
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 - Weighted PseudoF score
Labels - Object cluster assignments
Fs - Objective function values for the KM best iterate
Enorm - Average l2 norm of the residual norm.
tucker1966simuclustfactor \insertReft3clussimuclustfactor \insertRefVichiRocciKierssimuclustfactor
X_i_jk = generate_dataset()$X_i_jk model = simultaneous() t3clus = fit.t3clus(model, X_i_jk, c(8,5,4), c(3,3,2))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.