LLAhclust {LLAhclust} | R Documentation |
Builds a hierarchy from similarity coefficients among objects
or variables as returned by LLAsimvar
, LLAsimobj
or as.LLAsim
. The default aggregation criteria, called
lla
, can be regarded as a probabilistic version of the single linkage.
LLAhclust(s, method = "lla", epsilon = 1, members = NULL)
s |
Similarity coefficients as returned by LLAsimvar ,
LLAsimobj or as.LLAsim . |
method |
Linkage method (i.e. aggregation criterion). Can be one
of lla (default), tippett (Tippett's p-value
combination method), average , complete , fisher
(Fisher's p-value combination method), uniform
(uniform p-value combination method; can be regarded as a
probabilistic version of the average linkage), normal
(normal p-value combination method) or maximum (maximum p-value combination
method; can be regarded as a probabilistic version of the complete
linkage). See the last reference for more details.
|
epsilon |
Coefficient used in the lla linkage. Should lie
in [0,1]: epsilon=0
corresponds to the single linkage, epsilon=1 (default) yields
a probabilistic version of the single linkage. |
members |
"Weights" of the objects to be clustered if not of
equal "weight". See hclust for more details. |
An object of class hclust
with the corresponding
attributes. See hclust
for more details.
I.C. Lerman (1981), Classification et analyse ordinale de donnés, Dunod, Paris.
I.C. Lerman (1991), Foundations of the likelihood linkage analysis classification method, Applied Stochastic Models and Data Analysis, 7, pages 63–76.
I.C. Lerman (1993), Likelihood linkage analysis classification method: An example treated by hand, Biochimie, 75, pages 379–397.
I.C. Lerman, Ph. Peter and H. Leredde (1993), Principes et calculs de la méthode implantée dans le programme CHAVL (Classification Hiérarchique par Analyse de la Vraisemblance des Liens), Modulad, 12, pages 33-101.
I. Kojadinovic (2007), Hierarchical clustering of continuous variables based on the empirical copula process, submitted.
LLAsimvar
,
LLAsimobj
,
as.LLAsim
,
LLAparteval
,
hclust
.
data(USArrests) ## Compute similarities between variables based on ## the LLAnumerical method: s <- LLAsimvar(USArrests) s ## Perform the hierarchical clustering of the variables ## using the default aggregation criterion (lla): h <- LLAhclust(s) plot(h) ## Compute the quality of the partitions compatible ## with the hierarchy in terms of the statistics defined by Lerman: LLAparteval(h,s) ## Compute similarities between variables using the classical ## bilateral test of independence based on Spearman's rho: s <- LLAsimvar(USArrests, method = "spearman.abs") s ## Perform the hierarchical clustering of the variables ## using Fisher's p-value combination method: h <- LLAhclust(s,method="fisher") plot(h) ## NB: the height in the dendrogram is a p-value ## and can be used to identify mutually independent classes of ## variables, if any. ## Compute the quality of the partitions compatible ## with the hierarchy in terms of the indices defined in the ## last reference: LLAparteval(h,s)