T3Clusf {lsbclust} | R Documentation |
T3Clusf: Tucker3 Fuzzy Cluster Analysis
Description
This is an implementation of the T3Clusf algorithm of Rocci & Vichi (2005).
Usage
T3Clusf(X, Q, R = Q, G = 2, margin = 3L, alpha = 1, eps = 1e-08,
maxit = 100L, verbose = 1, nstart = 1L, parallel = TRUE,
mc.cores = detectCores() - 1L, minsize = 3L)
Arguments
X |
Three-way data array, with no missing values. |
Q |
Integer giving the number of dimensions required for mode B (variables).
This is the first mode of the array, excluding the mode clustered over (see |
R |
Integer giving the number of dimensions required for mode C (occasions).
This is the second mode of the array, excluding the mode clustered over (see |
G |
Integer giving the number of clusters required. |
margin |
Integer giving the margin of the array to cluster over. The remaining two
modes, in the original order, corresponds to |
alpha |
Numeric value giving the fuzziness parameter. |
eps |
Small numeric value giving the empirical convergence threshold. |
maxit |
Integer giving the maximum number of iterations allowed. |
verbose |
Integer giving the number of iterations after which the loss values are printed. |
nstart |
Integer giving the number of random starts required. |
parallel |
Logical indicating whether to parallelize over random starts if
|
mc.cores |
Argument passed to |
minsize |
Integer giving the minimum size of cluster to uphold when reinitializing empty clusters. |
References
Rocci, R., & Vichi, M. (2005). Three-mode component analysis with crisp or fuzzy partition of units. Psychometrika, 70(4), 715-736.
Examples
data("dcars")
set.seed(13)
res <- T3Clusf(X = carray(dcars), Q = 3, R = 2, G = 3, alpha = 1)