Description Details Slots Objects from the class

An S4 class representing a fitted mmpca model.

The BB-CVEM method is the branch & bound greedy procedure proposed in
the original paper of Jouvin et. al. https://arxiv.org/abs/1909.00721. The number of epochs in the `n_epochs`

slot
is actually the true number of pass minus 1 (unless `max.epochs`

was
reached). Indeed, the last pass before convergence does not change either
the bound or the `clustering`

, hence it is removed of the counter.

`call`

A

`call`

object specifying the call`method`

The method used in the call

`clustering`

The final partition found by the algorithm

`controls`

An object of class

`mmpcaClustcontrol`

containing the controls used in the VEM algorithm on the aggregated DTM during the loop. The slots`controls@control_lda_init`

where only use when init.beta == 'lda'.`K`

An integer specifying the number of topics.

`Q`

An integer specifying he number of clusters.

`N`

An integer specifying the number of observations.

`V`

An integer specifying the number of variables.

`beta`

The (KxV) topic matrix.

`gamma`

A (QxK) matrix containing the variational paramaters of the variational distribution of each $\theta_q$ in its rows.

`lda_algo`

An object of class "

`LDA`

" (cf.`TopicModel`

) containing the results of the`LDA`

() function applied to the aggregated DTM, with control`controls@control_lda_loop`

`max.epochs`

The maximum number of pass through the whole dataset in the algorithm.

`logLikelihoods`

A numeric vector containing the evolution of the variational bound every

`keep`

iteration.`keep`

An integer specifying the . Mostly useful for the plot function.

`n_epochs`

The number of pass through the datasets before convergence. see details

`llhood`

The final value of the variational lower bound.

`Yinit`

The value of the initial partition.

`icl`

The Integrated Classification Likelihood value.

Object of class "`mmpcaClust`

" are returned by
`mmpca_clust`

()

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