It estimates the OLBM model parameters as well as the most likely posterior cluster assignments by maximum likelihood.
olbm(Y, Q, L, init = "kmeans", eps = 1e-04, it_max = 500,
verbose = TRUE)An M x P ordinal matrix, containing ordinal entries from 1 to K. Missing data are coded as zeros.
The number of row clusters.
The number of column clusters.
A string specifying the initialisation type. It can be "kmeans" (the default) or "random" for a single random initialisation.
When the difference between two consecutive vaules of the log-likelihood is smaller than eps, the M-EM algorithms will stop.
The maximum number of iterations that the M-EM algorithms will perform (although the minimum tolerance eps is not reached).
A boolean specifying whether extended information should be displayed or not (TRUE by default).
It returns an S3 object of class "olbm" containing
the estimated row cluster memberships.
the estimated column cluster memberships.
the final value of the log-likelihood.
the value of the ICL criterion.
the Q x L estimated connectivity matrix.
a Q x L matrix containing the estimated means of the latent Gaussian distributions.
a Q x L matrix containing the estimated standard deviations of the latent Gaussian distributions.
a Q x L x K array whose entry (q,l,k) is the estimated probability that one user in the q-th row cluster assign the score k to one product in the l-th column cluster.
the estimated row cluster proportions.
the estimated column cluster proportions.
the initial row cluster assignments provided to the C-EM algorithm.
the initial column cluter assignments provided to the C-EM algorigthm.
the input ordinal matrix Y.
the values (1.5, 2.5, ... , K-0.5) of the thresholds, defined inside the function olbm.
Corneli M.,Bouveyron C. and Latouche P. (2019) Co-Clustering of ordinal data via latent continuous random variables and a classification EM algorithm. (https://hal.archives-ouvertes.fr/hal-01978174)
# NOT RUN {
data(olbm_dat)
res <- olbm(olbm_dat$Y, Q=3, L=2)
# }
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