Calculates Index of Dissimilarity — dissimilarity • segregation

Returns the total segregation between group and unit using the Index of Dissimilarity.

Usage

dissimilarity(
  data,
  group,
  unit,
  weight = NULL,
  se = FALSE,
  CI = 0.95,
  n_bootstrap = 100
)

Arguments

data: A data frame.
group: A categorical variable or a vector of variables contained in data. Defines the first dimension over which segregation is computed. The D index only allows two distinct groups.
unit: A categorical variable or a vector of variables contained in data. Defines the second dimension over which segregation is computed.
weight: Numeric. (Default NULL)
se: If TRUE, the segregation estimates are bootstrapped to provide standard errors and to apply bias correction. The bias that is reported has already been applied to the estimates (i.e. the reported estimates are "debiased") (Default FALSE)
CI: If se = TRUE, compute the confidence (CI*100) in addition to the bootstrap standard error. This is based on percentiles of the bootstrap distribution, and a valid interpretation relies on a larger number of bootstrap iterations. (Default 0.95)
n_bootstrap: Number of bootstrap iterations. (Default 100)

Value

Returns a data.table with one row. The column est contains the Index of Dissimilarity. If se is set to TRUE, an additional column se contains the associated bootstrapped standard errors, an additional column CI contains the estimate confidence interval as a list column, an additional column bias contains the estimated bias, and the column est contains the bias-corrected estimates.

References

Otis Dudley Duncan and Beverly Duncan. 1955. "A Methodological Analysis of Segregation Indexes," American Sociological Review 20(2): 210-217.

Examples

# Example where D and H deviate
m1 <- matrix_to_long(matrix(c(100, 60, 40, 0, 0, 40, 60, 100), ncol = 2))
m2 <- matrix_to_long(matrix(c(80, 80, 20, 20, 20, 20, 80, 80), ncol = 2))
dissimilarity(m1, "group", "unit", weight = "n")
#>    stat est
#> 1:    D 0.6
dissimilarity(m2, "group", "unit", weight = "n")
#>    stat est
#> 1:    D 0.6