Returns local segregation indices for each category defined
by unit.
Usage
mutual_local(
data,
group,
unit,
weight = NULL,
se = FALSE,
CI = 0.95,
n_bootstrap = 100,
base = exp(1),
wide = FALSE
)Arguments
- data
A data frame.
- group
A categorical variable or a vector of variables contained in
data. Defines the dimension over which segregation is computed.- unit
A categorical variable or a vector of variables contained in
data. Defines the group for which local segregation indices are calculated.- 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") (DefaultFALSE)- 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. (Default0.95)- n_bootstrap
Number of bootstrap iterations. (Default
100)- base
Base of the logarithm that is used in the calculation. Defaults to the natural logarithm.
- wide
Returns a wide dataframe instead of a long dataframe. (Default
FALSE)
Value
Returns a data.table with two rows for each category defined by unit,
for a total of 2*(number of units) rows.
The column est contains two statistics that
are provided for each unit: ls, the local segregation score, and
p, the proportion of the unit from the total number of cases.
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.
If wide is set to TRUE, returns instead a wide dataframe, with one
row for each unit, and the associated statistics in separate columns.
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
# which schools are most segregated?
(localseg <- mutual_local(schools00, "race", "school",
weight = "n", wide = TRUE
))
#> school ls p
#> 1: A1_1 0.1826710 0.0004522985
#> 2: A1_2 0.1825592 0.0004978701
#> 3: A1_3 0.2756157 0.0006642066
#> 4: A1_4 0.1368034 0.0005685061
#> 5: A2_1 0.3585546 0.0004260948
#> ---
#> 2041: C165_1 0.3174930 0.0004568556
#> 2042: C165_2 0.3835477 0.0005297702
#> 2043: C165_3 0.2972550 0.0005650883
#> 2044: C166_1 0.3072281 0.0011586588
#> 2045: C167_1 0.3166498 0.0005354667
sum(localseg$p) # => 1
#> [1] 1
# the sum of the weighted local segregation scores equals
# total segregation
sum(localseg$ls * localseg$p) # => .425
#> [1] 0.425539
mutual_total(schools00, "school", "race", weight = "n") # M => .425
#> stat est
#> 1: M 0.42553898
#> 2: H 0.05642991