| fit.t3clus {simuclustfactor} | R Documentation |
T3Clus Model
Description
Implements simultaneous version of TWCFTA
Usage
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)
Arguments
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. |
Details
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.
Value
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.
References
Tucker L (1966). “Some mathematical notes on three-mode factor analysis.” Psychometrika, 31(3), 279-311. doi:10.1007/BF02289464, https://ideas.repec.org/a/spr/psycho/v31y1966i3p279-311.html. Rocci R, Vichi M (2005). “Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units.” Psychometrika, 70, 715-736. doi:10.1007/s11336-001-0926-z. Vichi M, Rocci R, Kiers H (2007). “Simultaneous Component and Clustering Models for Three-way Data: Within and Between Approaches.” Journal of Classification, 24, 71-98. doi:10.1007/s00357-007-0006-x.
Examples
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))