complexity_measures: Complexity Measures

In pachamaltese/binet: Functions to build bipartite networks under the Tidyverse philosophy

Description

complexity_measures() computes different complexity measures obtained from the Balassa Index or a binary (0/1) metric for a bipartite relation between two disjoint sets X, the "source" or "from" side, and Y, the "target" or "to" side.

Usage

complexity_measures(balassa_index, method = "fitness", iterations = 20,
  extremality = 1)

Arguments

balassa_index	(Type: matrix or dgCMatrix) the output from balassa_index()) or an equivalent arrangement.
method	(Type: character) one of these methods: fitness, reflections or eigenvalues. By default this is set to "fitness".
iterations	(Type: numeric) the number of iterations to use. By default this is set to 20.
extremality	(Type: numeric) the parameter to use in the fitness method. The other methods don't use this parameter. By default this is set to 1.

Details

The current implementation follows \insertCitemeasuringcomplexity2015binet to obtain different metrics that account for diversification in bipartite relations.

Value

A list of four data frames. Two complexity indexes that are ordering rankings for specialization and two aggregations (sums) of the Balassa Index.

References

For more information on this index see:

\insertRef

measuringcomplexity2015binet

and the references therein.

Examples

complexity_measures(balassa_index = binet_output$balassa_index)

pachamaltese/binet documentation built on Jan. 16, 2020, 2:02 a.m.