to_project {manynet}

Modifying networks projection

Description

These functions offer tools for projecting manynet-consistent data:

• to_mode1() projects a two-mode network to a one-mode network of the first node set's (e.g. rows) joint affiliations to nodes in the second node set (columns).

• to_mode2() projects a two-mode network to a one-mode network of the second node set's (e.g. columns) joint affiliations to nodes in the first node set (rows).

• to_ties() projects a network to one where the ties become nodes and incident nodes become their ties.

• to_galois() projects a network to its Galois derivation.

Usage

to_mode1(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))

to_mode2(.data, similarity = c("count", "jaccard", "rand", "pearson", "yule"))

to_ties(.data)

to_galois(.data)

Arguments

.data

An object of a manynet-consistent class:

• matrix (adjacency or incidence) from {base} R

• edgelist, a data frame from {base} R or tibble from {tibble}

• igraph, from the {igraph} package

• network, from the {network} package

• tbl_graph, from the {tidygraph} package

similarity

Method for establishing ties, currently "count" (default), "jaccard", or "rand". "count" calculates the number of coinciding ties, and can be interpreted as indicating the degree of opportunities between nodes. "jaccard" uses this count as the numerator in a proportion, where the denominator consists of any cell where either node has a tie. It can be interpreted as opportunity weighted by participation. "rand", or the Simple Matching Coefficient, is a proportion where the numerator consists of the count of cells where both nodes are present or both are absent, over all possible cells. It can be interpreted as the (weighted) degree of behavioral mirroring between two nodes. "pearson" (Pearson's coefficient) and "yule" (Yule's Q) produce correlations for valued and binary data, respectively. Note that Yule's Q has a straightforward interpretation related to the odds ratio.

Details

Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:

	data.frame	igraph	matrix	network	tbl_graph
to_mode1	1	1	1	1	1
to_mode2	1	1	1	1	1
to_ties	1	1	1	1	1

Value

All to_ functions return an object of the same class as that provided. So passing it an igraph object will return an igraph object and passing it a network object will return a network object, with certain modifications as outlined for each function.

Galois lattices

Note that the output from to_galois() is very busy at the moment.

See Also

Other modifications: add_nodes(), add_ties(), as(), correlation, from, miss, reformat, split(), to_levels, to_paths, to_scope

Examples

to_mode1(ison_southern_women)
to_mode2(ison_southern_women)
#graphr(to_mode1(ison_southern_women))
#graphr(to_mode2(ison_southern_women))
to_ties(ison_adolescents)
#graphr(to_ties(ison_adolescents))