olbm {ordinalLBM}

Fitting OLBM to the data

Description

It estimates the OLBM model parameters as well as the most likely posterior cluster assignments by maximum likelihood.

Usage

olbm(Y, Q, L, init = "kmeans", eps = 1e-04, it_max = 500,
  verbose = TRUE)

Arguments

Y

An M x P ordinal matrix, containing ordinal entries from 1 to K. Missing data are coded as zeros.

Q

The number of row clusters.

L

The number of column clusters.

init

A string specifying the initialisation type. It can be "kmeans" (the default) or "random" for a single random initialisation.

eps

When the difference between two consecutive vaules of the log-likelihood is smaller than eps, the M-EM algorithms will stop.

it_max

The maximum number of iterations that the M-EM algorithms will perform (although the minimum tolerance eps is not reached).

verbose

A boolean specifying whether extended information should be displayed or not (TRUE by default).

Value

It returns an S3 object of class "olbm" containing

estR

the estimated row cluster memberships.

estC

the estimated column cluster memberships.

likeli

the final value of the log-likelihood.

icl

the value of the ICL criterion.

Pi

the Q x L estimated connectivity matrix.

mu

a Q x L matrix containing the estimated means of the latent Gaussian distributions.

sd

a Q x L matrix containing the estimated standard deviations of the latent Gaussian distributions.

eta

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.

rho

the estimated row cluster proportions.

delta

the estimated column cluster proportions.

initR

the initial row cluster assignments provided to the C-EM algorithm.

initC

the initial column cluter assignments provided to the C-EM algorigthm.

Y

the input ordinal matrix Y.

thresholds

the values (1.5, 2.5, ... , K-0.5) of the thresholds, defined inside the function olbm.

References

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)

Examples

data(olbm_dat)
res <- olbm(olbm_dat$Y, Q=3, L=2)