galois {multiplex}

Galois Derivations Between Subsets

Description

Function to perform Galois derivations between partially ordered subsets

Usage

galois(x, labeling = c("full", "reduced"), sep, valued, scl, 
    sep2)

Arguments

x

a data frame with objects and attributes

labeling

whether the derivations should be

• full for full labeling

• reduced for reduced labeling

sep

(optional) the pair separator for the pairwise relations

valued

(logical) whether the galois derivation is on a many-valued formal context

scl

(optional, only for valued) the scale to be used in the galois derivation

sep2

(optional, only for valued) the separator in the formal concept

Details

Galois derivations (or connections) are mappings between families of partially ordered subsets of elements. Such derivations are useful to analyze the structure of both subsets, which in a social network are typically the actors and their corresponding affiliations or events. That is, two-mode networks, but also a group of objects with a list of different attributes as used in formal concept analysis.

Value

A labelled list with Galois derivations of objects and attributes

Note

Full labeling implies first objects and then attributes, whereas the reduced option is given the other way around.

Author(s)

Antonio Rivero Ostoic

References

Ganter, B. and R. Wille Formal Concept Analysis – Mathematical Foundations. Springer. 1996.

See Also

partial.order, diagram, fltr.

Examples

## Create a data frame
dfr <- data.frame(x=1:3, y=5:7)

## Find Galois derivations
galois(dfr)