redist (version 2.0.2)

redist.segcalc: Segregation index calculation for MCMC redistricting.

Description

redist.segcalc calculates the dissimilarity index of segregation (see Massey \& Denton 1987 for more details) for a specified subgroup under any redistricting plan.

Usage

redist.segcalc(algout, grouppop, fullpop)

Arguments

algout

A matrix of congressional district assignments or a redist object.

grouppop

A vector of populations for some subgroup of interest.

fullpop

A vector containign the populations of each geographic unit.

Value

redist.segcalc returns a vector where each entry is the dissimilarity index of segregation (Massey & Denton 1987) for each redistricting plan in algout.

References

Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain Monte Carlo." Working Paper. Available at http://imai.princeton.edu/research/files/redist.pdf.

Massey, Douglas and Nancy Denton. (1987) "The Dimensions of Social Segregation". Social Forces.

Examples

# NOT RUN {
data(algdat.pfull)

## Code to run the simulations in Figure 4 of Fifield, Higgins,
## Imai and Tarr (2015)

## Get an initial partition
set.seed(1)
initcds <- algdat.pfull$cdmat[,sample(1:ncol(algdat.pfull$cdmat), 1)]

## Run simulations
alg_253 <- redist.mcmc(adjobj = algdat.pfull$adjlist,
popvec = algdat.pfull$precinct.data$pop,
initcds = initcds, nsims = 10000)

## Get Republican Dissimilarity Index from simulations
rep_dmi_253 <- redist.segcalc(alg_253,
algdat.pfull$precinct.data$repvote,
algdat.pfull$precinct.data$pop)
# }