An R package to calculate and decompose entropy-based, multigroup segregation indices, with a focus on the Mutual Information Index (M) and Theil’s Information Index (H).
The package provides an easy way to calculate segregation measures, based on the Mutual Information Index (M) and Theil’s Entropy Index (H).
Standard errors in all functions can be estimated via boostrapping:
Decompose segregation into a between-state and a within-state term (the sum of these equals total segregation):
Local segregation (
ls) is a decomposition by units (here racial groups). The sum of the proportion-weighted local segregation scores equals M:
(local <- mutual_local(schools00, group = "school", unit = "race", weight = "n", se = TRUE, wide = TRUE)) #> race ls ls_se p p_se #> 1 asian 0.667 0.006736 0.02261 0.000124 #> 2 black 0.885 0.002595 0.19005 0.000465 #> 3 hisp 0.782 0.002582 0.15179 0.000317 #> 4 white 0.184 0.000725 0.62810 0.000687 #> 5 native 1.528 0.022868 0.00745 0.000135 sum(local$p * local$ls) #>  0.429
Decompose the difference in M between 2000 and 2005, using iterative proportional fitting (IPF) and the Shapley decomposition, as suggested by Karmel and Maclachlan (1988) and Deutsch et al. (2006):
mutual_difference(schools00, schools05, group = "race", unit = "school", weight = "n", method = "shapley") #> stat est #> M1 M1 0.42554 #> M2 M2 0.41339 #> diff diff -0.01215 #> additions additions -0.00341 #> removals removals -0.01141 #> group_marginal group_marginal 0.01623 #> unit_marginal unit_marginal -0.01674 #> structural structural 0.00318
Find more information in the documentation.
To install the package from CRAN, use
To install the development version, use
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