Description Usage Arguments Value Examples
View source: R/mmpca_clust_modelselection.R
A wrapper on mmpca_clust
() to perform model
selection with an Integrated Classification Likelihood (ICL) criterion.
1 2 3 4 5 6 7 8 9 10 11 12 13 | mmpca_clust_modelselect(
dtm,
Qs,
Ks,
Yinit = "random",
method = "BBCVEM",
init.beta = "lda",
keep = 1L,
max.epochs = 10L,
verbose = 1L,
nruns = 5L,
mc.cores = (detectCores() - 1)
)
|
dtm |
an NxV |
Qs |
The vector of clusters to be tested. |
Ks |
The number of topics to be tested. |
Yinit |
Parameter for the initialization of Y. It can be either:
|
method |
The clustering algorithm to be used. Only "BBCVEM" is available : it corresponds to the branch and bound C-VEM of the original article. |
init.beta |
Parameter for the initialization of the matrix beta. It can be either:
|
keep |
The evolution of the bound is tracked every |
max.epochs |
Specifies the maximum number of pass allowed on the whole dataset. |
verbose |
verbosity level. |
nruns |
number of runs of the algorithm for each (K,Q) pair (default to 1) : the run achieving the best evidence lower bound is selected. |
mc.cores |
The number of CPUs to use when fitting in parallel the different models. Default is the number of available cores minus 1. |
An object of class "mmpcaClust"
containing the best selected model.
A matrix containing the value of the ICL for each pair (K,Q).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## generate data with the BBCmsg
simu = simulate_BBC(N = 100, L = 250)
## Define a grid
Qs = 5:6
Ks = 3:4
## Run model selection with MoMPCA
res <- mmpca_clust_modelselect(simu$dtm.full, Qs = Qs, Ks = Ks,
Yinit = 'kmeans_lda',
init.beta = 'lda',
method = 'BBCVEM',
max.epochs = 7,
nruns = 2,
verbose = 1,
mc.cores = 2)
|
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