Clustering of Variables Around Latent Variables
Scaling of a three-way array
Boostrapping for assessing the stability of a CLV result
Hierarchical clustering of variables with consolidation
K-means algorithm for the clustering of variables
Hierarchical clustering of variables (associated with mode 2 three-way...
Partitioning algorithm of a set of variables (associated with mode 2) ...
biplot for the dataset
latent components associated with each cluster
Loadings of the variables on the latent component, in each cluster.
clusters memberships for a partition into K clusters.
Weights of the external variables, or additional mode, on the latent c...
Imputation of a data matrix based on CLV results
L-CLV for L-shaped data
linear model based on CLV
Graphical representation of the CLV clustering stages
Graphical representation of the CLV3W hierarchical clustering stages
Graphical representation of the LCLV clustering stages
Scores plot from a Candecomp Parafac analysis. The group membership of...
Representation of the variables and their group membership
prediction for lmCLV models.
Print the CLV results
Print the CLV3W results
Print the LCLV results
Standardization of the qualitative variables
summary and description of the clusters of variables
Summary and description of the clusters of (mode 2) variables associat...
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.