compress: Compresses a data matrix based on mutual information...

In elbersb/mutual: Entropy-Based Segregation Indices

Compresses a data matrix based on mutual information (segregation)

Description

Given a data set that identifies suitable neighbors for merging, this function will merge units iteratively, where in each iteration the neighbors with the smallest reduction in terms of total M will be merged.

Usage

compress(
  data,
  group,
  unit,
  weight = NULL,
  neighbors = "local",
  n_neighbors = 50,
  max_iter = Inf
)

Arguments

data	A data frame.
group	A categorical variable contained in data. Defines the first dimension over which segregation is computed.
unit	A categorical variable contained in data. Defines the second dimension over which segregation is computed.
weight	Numeric. Only frequency weights are allowed. (Default NULL)
neighbors	Either a data frame or a character. If data frame, then it needs exactly two columns, where each row identifies a set of "neighbors" that may be merged. If "local", considers the n_neighbors closest neighbors in terms of local segregation. If "all", all units are considered as possible neighbors. This may be very time-consuming.
n_neighbors	Only relevant if neighbors is "local".
max_iter	Maximum number of iterations (Default Inf)

Value

Returns a data.table.

elbersb/mutual documentation built on Feb. 12, 2024, 6:40 a.m.