redist.combine.mpi: Combine successive runs of 'redist.mcmc.mpi'

In redist: Simulation Methods for Legislative Redistricting

Combine successive runs of redist.mcmc.mpi

Description

redist.combine.mpi is used to combine successive runs of redist.mcmc.mpi into a single data object

Usage

redist.combine.mpi(savename, nloop, nthin, tempadj)

Arguments

savename	The name (without the loop or .RData suffix) of the saved simulations.
nloop	The number of loops being combined.
nthin	How much to thin the simulations being combined.
tempadj	The temperature adjacency object saved by redist.mcmc.mpi.

Details

This function allows users to combine multiple successive runs of redist.mcmc.mpi into a single redist object for analysis.

Value

redist.combine.mpi returns an object of class "redist". The object redist is a list that contains the following components (the inclusion of some components is dependent on whether tempering techniques are used):

plans	Matrix of congressional district assignments generated by the algorithm. Each row corresponds to a geographic unit, and each column corresponds to a simulation.
distance_parity	Vector containing the maximum distance from parity for a particular simulated redistricting plan.
mhdecisions	A vector specifying whether a proposed redistricting plan was accepted (1) or rejected (0) in a given iteration.
mhprob	A vector containing the Metropolis-Hastings acceptance probability for each iteration of the algorithm.
pparam	A vector containing the draw of the p parameter for each simulation, which dictates the number of swaps attempted.
constraint_pop	A vector containing the value of the population constraint for each accepted redistricting plan.
constraint_compact	A vector containing the value of the compactness constraint for each accepted redistricting plan.
constraint_vra	A vector containing the value of the vra constraint for each accepted redistricting plan.
constraint_similar	A vector containing the value of the similarity constraint for each accepted redistricting plan.
constraint_qps	A vector containing the value of the QPS constraint for each accepted redistricting plan.
beta_sequence	A vector containing the value of beta for each iteration of the algorithm. Returned when tempering is being used.
mhdecisions_beta	A vector specifying whether a proposed beta value was accepted (1) or rejected (0) in a given iteration of the algorithm. Returned when tempering is being used.
mhprob_beta	A vector containing the Metropolis-Hastings acceptance probability for each iteration of the algorithm. Returned when tempering is being used.

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.

Examples

## Not run: 
# Cannot run on machines without Rmpi
data(fl25)
data(fl25_enum)
data(fl25_adj)

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

## Get an initial partition
init_plan <- fl25_enum$plans[, 5118]

## Run the algorithm
redist.mcmc.mpi(adj = fl25_adj, total_pop = fl25$pop,
    init_plan = init_plan, nsims = 10000, nloops = 2, savename = "test")
out <- redist.combine.mpi(savename = "test", nloop = 2,
    nthin = 10, tempadj = tempAdjMat)

## End(Not run)

redist documentation built on June 24, 2024, 9:10 a.m.