LLAsimvar {LLAhclust} | R Documentation |
Computes similarities among variables using the likelihood
linkage analysis approach proposed by Lerman. The likelihood
linkage analysis method mainly consists in replacing the value
of the estimated similarity coefficient between two variables
by the probability of finding a lower value under the
hypothesis of stochastic independence, called
absence of link in that context. Nine similarity coefficients
can be computed using the LLAsimvar
function.
LLAsimvar(x, method = "LLAnumerical", upper = FALSE, simulated.distribution = NULL)
x |
a numeric matrix or data frame. |
method |
Can be one of LLAnumerical ,
LLAcategorical , LLAordinal , LLAboolean ,
chi.square , pearson.abs ,
spearman.abs , kendall.abs or
empirical.copula . The methods LLA* were initially
defined by Lerman (see references below). The four remaining methods
compute the similarity between two variables as one minus the p-value
obtained from a test of independence. See the last reference and the
example section below for more details. |
upper |
logical value indicating whether the upper triangle of the
similarity matrix should be printed by print.LLAsim . |
simulated.distribution |
Object of class
empcopula.simulation .
Should be set only if the method empirical.copula is selected.
See function empcopula.simulate and the example
section below for more details.
|
The following functions are also defined for objects of class
LLAsim
: names.LLAsim
, format.LLAsim
,
as.matrix.LLAsim
and print.LLAsim
.
Returns an object of class LLAsim
whose attributes are
very similar to those of objects of class dist
. See
dist
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.
P. Deheuvels (1979), La fonction de dépendance empirique et ses propriétés: un test non paramétrique d'indépendance, Acad. Roy. Belg. Bull. Cl. Sci. 5th Ser. 65, 274-292.
C. Genest and B. Rémillard (2004). Tests of independence and randomness based on the empirical copula process. Test, 13, 335-369.
I. Kojadinovic (2007), Hierarchical clustering of continuous variables based on the empirical copula process, submitted.
as.LLAsim
,
empcopula.simulate
,
LLAsimobj
,
LLAhclust
,
LLAparteval
,
dist
.
data(USArrests) ## Compute similarities between variables using the ## LLAnumerical method: s <- LLAsimvar(USArrests) s ## Compute similarities between variables using the classical ## bilateral test of independence based on Spearman's rho: s <- LLAsimvar(USArrests, method = "spearman.abs") s ## Compute similarities between variables using the classical ## bilateral test of independence based on Kendall's tau: s <- LLAsimvar(USArrests, method = "kendall.abs") s ## Compute similarities between variables using the test of ## independence e la Deheuvels based on the empirical copula ## process recently studied by Genest and Remillard: s <- LLAsimvar(USArrests, method = "empirical.copula") s ## The previous computation could have been done in two steps: d <- empcopula.simulate(n=50,N=2000) s <- LLAsimvar(USArrests, method = "empirical.copula", simulated.distribution = d) s