relations (version 0.6-8)

ensemble: Relation Ensembles

Description

Creation and manipulation of relation ensembles.

Usage

relation_ensemble(..., list = NULL)
as.relation_ensemble(x)
is.relation_ensemble(x)

Arguments

…

R objects representing relations, or coercible to such.

list

a list of R objects as in ….

x

for coercion with as.relation_ensemble(), an R object as in …; for testing with is.relation_ensemble(), an arbitrary R object.

Details

relation_ensemble() creates non-empty “relation ensembles”, i.e., collections of relations \(R_i = (D, G_i)\) with the same domain \(D\) and possibly different graphs \(G_i\).

Such ensembles are implemented as suitably classed lists of relation objects, making it possible to use lapply() for computations on the individual relations in the ensemble. Available methods for relation ensembles include those for subscripting, c(), t(), rep(), and print().

Examples

# NOT RUN {
data("Cetacea")
## Consider each variable an equivalence relation on the objects.
## Note that 2 variables (LACHRYMAL_AND_JUGAL_BONES and HEAD_BONES) have
## missing values, and hence are excluded.
ind <- sapply(Cetacea, function(s) all(!is.na(s)))
relations <- as.relation_ensemble(Cetacea[, ind])
## This gives a relation ensemble of length 14 (number of complete
## variables in the data set).
print(relations)
## Are there any duplicated relations?
any(duplicated(relations))
## Replicate and combine ...
thrice <- c(rep(relations, 2), relations)
## Extract unique elements again:
all.equal(unique(thrice), relations)
## Note that unique() does not preserve attributes, and hence names.
## In case we want otherwise:
all.equal(thrice[!duplicated(thrice)], relations)
## Subscripting:
relation_dissimilarity(relations[1 : 2], relations["CLASS"])
## Which relation is "closest" to the classification?
d <- relation_dissimilarity(relations)
sort(as.matrix(d)[, "CLASS"])[-1]
# }

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