A class for CoClust
and its extensions
Objects can be created by calls of the form new("CoClust", ...)
.
Number.of.Clusters
:Object of class "integer"
. The number K of identified clusters.
Index.Matrix
:Object of class "matrix"
. A n.obs by (K+1) matrix where n.obs is the number of observations put in each cluster. The matrix contains the row indexes of the observations of the data matrix m
.
The last column contains the log-likelihood of the copula fit.
Data.Clusters
:Object of class "matrix"
. The matrix of the final clustering.
Dependence
:Object of class "list"
. The list contains:
Model
the copula model used for the clustering.
Param
the estimated dependence parameter between clusters.
Std.Err
the standard error of Param.
P.val
the p-value associated to the null hypothesis H_0: theta=0
.
LogLik
:Object of class "numeric"
. The maximized log-likelihood copula fit.
Est.Method
:Object of class "character"
. The estimation method used for the copula fit.
Opt.Method
:Object of class "character"
. The optimization method used for the copula fit.
LLC
:Object of class "numeric"
. The value of the LogLikelihood Criterion for each k in dimset
.
Index.dimset
:Object of class "list"
. A list that, for each k in dimset
, contains the index matrix of the initial set of nk
observations used for selecting the number of clusters, together with the associated loglikelihood.
No methods defined with class "CoClust" in the signature.
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>,
Simone Giannerini <simone.giannerini@unibo.it>
Di Lascio, F.M.L. and Giannerini, S. (2019). "Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. DOI 10.1007/s00362-016-0822-3.
Di Lascio, F.M.L. (2018) "CoClust: An R Package for Copula-based Cluster Analysis", Recent Applications in Data Clustering, p.93-114. Ed. Harun Pirim, IntTech Publisher. ISBN 978-1-78923-527-2. DOI 10.5772/intechopen.74865.
Di Lascio, F.M.L., Durante, F. and Pappada', R. (2017). "Copula-based clustering methods", Copulas and Dependence Models with Applications, p.49-67. Eds Ubeda-Flores, M., de Amo, E., Durante, F. and Fernandez Sanchez, J., Springer International Publishing. ISBN: 978-3-319-64220-8.
Di Lascio, F.M.L. and Disegna, M. (2017). "A copula-based clustering algorithm to analyse EU country diets". Knowledge-Based Systems, 132, p.72-84. DOI: 10.1016/j.knosys.2017.06.004.
Di Lascio, F.M.L. and Giannerini, S. (2012). "A Copula-Based Algorithm for Discovering Patterns of Dependent Observations", Journal of Classification, 29(1), p.50-75.
Di Lascio, F.M.L. (2008). "Analyzing the dependence structure of microarray data: a copula-based approach". PhD thesis, Dipartimento di Scienze Statistiche, Universita' di Bologna, Italy.
See Also CoClust
.