Description Usage Arguments Value Examples
This function is an implementation of Multiple Latent Components Clustering
(MLCC) algorithm which clusteres quantitative variables into a number, chosen
using mBIC, of groups. For each considered number of clusters in
numb.clusters mlcc.reps
function is called. It invokes
Kmeans based algorithm (mlcc.kmeans
) finding local minimum of
mBIC, which is run a given number of times (numb.runs) with different
initializations. The best partition is choosen with mBIC (see
mlcc.reps
function).
1 2 3 4 
X 
A data frame or a matrix with only continuous variables. 
numb.clusters 
A vector, numbers of clusters to be checked. 
numb.runs 
An integer, number of runs (initializations) of

stop.criterion 
An integer, if an iteration of

max.iter 
An integer, maximum number of iterations of the loop in

max.dim 
An integer, if estimate.dimensions is FALSE then max.dim is dimension of each subspace. If estimate.dimensions is TRUE then subspaces dimensions are estimated from the range [1, max.dim]. 
scale 
A boolean, if TRUE (value set by default) then variables in dataset are scaled to zero mean and unit variance. 
numb.cores 
An integer, number of cores to be used, by default all cores are used. 
greedy 
A boolean, if TRUE (value set by default) the clusters are estimated in a greedy way  first local minimum of mBIC is chosen. 
estimate.dimensions 
A boolean, if TRUE (value set by default) subspaces dimensions are estimated. 
verbose 
A boolean, if TRUE plot with mBIC values for different numbers of clusters is produced and values of mBIC, computed for every number of clusters and subspaces dimensions, are printed (value set by default is FALSE). 
flat.prior 
A boolean, if TRUE then, instead of an informative prior that takes into account number of models for a given number of clusters, flat prior is used. 
show.warnings 
A boolean, if set to TRUE all warnings are displayed, default value is FALSE. 
An object of class mlcc.fit consisting of
segmentation 
a vector containing the partition of the variables 
BIC 
numeric, value of mBIC 
subspacesDimensions 
a list containing dimensions of the subspaces 
nClusters 
an integer, estimated number of clusters 
factors 
a list of matrices, basis for each subspace 
all.fit 
a list of segmentation, mBIC, subspaces dimension for all numbers of clusters considered for an estimated subspace dimensions 
all.fit.dims 
a list of lists of segmentation, mBIC, subspaces dimension for all numbers of clusters and subspaces dimensions considered 
1 2 3  sim.data < data.simulation(n = 50, SNR = 1, K = 3, numb.vars = 50, max.dim = 3)
mlcc.res < mlcc.bic(sim.data$X, numb.clusters = 1:5, numb.runs = 20, numb.cores = 1, verbose=TRUE)
show.clusters(sim.data$X, mlcc.res$segmentation)

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