Function reference • segregation

Segregation indices

dissimilarity(): Calculates Index of Dissimilarity

exposure(): Calculates pairwise exposure indices

isolation(): Calculates isolation indices

mutual_total(): Calculates the Mutual Information Index M and Theil's Entropy Index H

mutual_total_nested(): Calculates a nested decomposition of segregation for M and H

mutual_within(): Calculates detailed within-category segregation scores for M and H

mutual_local(): Calculates local segregation scores based on M

Visualizing segregation

segcurve(): A visual representation of two-group segregation

segplot(): A visual representation of segregation

Debiasing

mutual_expected(): Calculates expected values when true segregation is zero

dissimilarity_expected(): Calculates expected values when true segregation is zero

Comparing differences

mutual_difference(): Decomposes the difference between two M indices

ipf(): Adjustment of marginal distributions using iterative proportional fitting

Compressing segregation

compress(): Compresses a data matrix based on mutual information (segregation)

merge_units(): Creates a compressed dataset

get_crosswalk(): Create crosswalk after compression

scree_plot(): Scree plot for segregation compression

Datasets

school_ses: Student-level data including SES status

schools00: Ethnic/racial composition of schools for 2000/2001

schools05: Ethnic/racial composition of schools for 2005/2006

Helper functions

entropy(): Calculates the entropy of a distribution

matrix_to_long(): Turns a contingency table into long format