# kmlcov: Clustering longitudinal data using the likelihood as a metric of distance

'kmlcov' Cluster longitudinal data using the likelihood as a metric of distance. The generalised linear model allow the user to introduce covariates with different level effects (2 levels).

- Author
- Mamoun O. Benghezal [aut, cre], Christophe Genolini [ctb]
- Date of publication
- 2013-08-21 16:07:27
- Maintainer
- Mamoun O. Benghezal <mobenghezal@gmail.com>
- License
- GPL-2
- Version
- 1.0.1

## Man pages

- addIndic
- Create the new formula with the indicator covariates
- affect_rand
- Affect randomly the individuals to the clusters
- artifdata
- Artificial data
- Converge-class
- Class '"Converge"'
- Converge-methods
- Method for function 'show'
- getNomCoef
- Get the name of the coefficients in the 'glm' object...
- glmClust
- Clustering longitudinal data
- GlmCluster-class
- Class 'GlmCluster'
- GlmCluster-methods
- Plot the main trajectories
- kmlCov
- Clustering longitudinal data from different starting...
- KmlCovList-class
- Class 'KmlCovList'
- KmlCovList-methods
- Plot the main trajectories
- kmlcov-package
- Clustering longitudinal data using the likelihood as a metric...
- log_lik
- Calculate the log-likelihood
- majIndica
- Calculate an indicator vector
- predict_clust
- Creates a character string expression to calculate the...
- rwFormula
- Rewrite the formula with all the covariates
- seperateFormula
- Separate the covariates in a formula
- which_best
- Seek the best partitions