Returns the total segregation between group and unit. If within is given, calculates segregation within each within category separately, and takes the weighted average. Also see mutual_within for detailed within calculations.

mutual_total(
data,
group,
unit,
within = NULL,
weight = NULL,
se = FALSE,
CI = 0.95,
n_bootstrap = 100,
base = exp(1)
)

## 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.

unit

A categorical variable or a vector of variables contained in data. Defines the second dimension over which segregation is computed.

within

A categorical variable or a vector of variables contained in data. The variable(s) should be a superset of either the unit or the group for the calculation to be meaningful. If provided, segregation is computed within the groups defined by the variable, and then averaged. (Default NULL)

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)

base

Base of the logarithm that is used in the calculation. Defaults to the natural logarithm.

## Value

Returns a data.table with two rows. The column est contains the Mutual Information Index, M, and Theil's Entropy Index, H. The H is the the M divided by the group entropy. If within was given, M and H are weighted averages of the within-category segregation scores. 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

Henri Theil. 1971. Principles of Econometrics. New York: Wiley.

Ricardo Mora and Javier Ruiz-Castillo. 2011. "Entropy-based Segregation Indices". Sociological Methodology 41(1): 159–194.

## Examples

# calculate school racial segregation
mutual_total(schools00, "school", "race", weight = "n") # M => .425
#>    stat        est
#> 1:    M 0.42553898
#> 2:    H 0.05642991

# note that the definition of groups and units is arbitrary
mutual_total(schools00, "race", "school", weight = "n") # M => .425
#>    stat       est
#> 1:    M 0.4255390
#> 2:    H 0.4188083

# if groups or units are defined by a combination of variables,
# vectors of variable names can be provided -
# here there is no difference, because schools
# are nested within districts
mutual_total(schools00, "race", c("district", "school"),
weight = "n") # M => .424
#>    stat       est
#> 1:    M 0.4255390
#> 2:    H 0.4188083

# estimate standard errors and 95% CI for M and H
if (FALSE) {
mutual_total(schools00, "race", "school", weight = "n",
se = TRUE, n_bootstrap = 1000)
}

# estimate segregation within school districts
mutual_total(schools00, "race", "school",
within = "district", weight = "n") # M => .087
#>    stat        est
#> 1:    M 0.08758648
#> 2:    H 0.08620114

# estimate between-district racial segregation
mutual_total(schools00, "race", "district", weight = "n") # M => .338
#>    stat       est
#> 1:    M 0.3379525
#> 2:    H 0.3326072
# note that the sum of within-district and between-district
# segregation equals total school-race segregation;
# here, most segregation is between school districts