redist.flip.anneal simulates congressional redistricting plans using Markov chain Monte Carlo methods coupled with simulated annealing.

redist.flip.anneal(
  adj,
  total_pop,
  ndists = NULL,
  init_plan = NULL,
  constraints = redist_constr(),
  num_hot_steps = 40000,
  num_annealing_steps = 60000,
  num_cold_steps = 20000,
  eprob = 0.05,
  lambda = 0,
  pop_tol = NULL,
  rngseed = NULL,
  maxiterrsg = 5000,
  adapt_lambda = FALSE,
  adapt_eprob = FALSE,
  exact_mh = FALSE,
  savename = NULL,
  verbose = TRUE
)

Arguments

adj

adjacency matrix, list, or object of class "SpatialPolygonsDataFrame."

total_pop

A vector containing the populations of each geographic unit

ndists

The number of congressional districts. The default is NULL.

init_plan

A vector containing the congressional district labels of each geographic unit. If not provided, random and contiguous congressional district assignments will be generated using redist_smc. To use the old behavior of generating with redist.rsg, provide init_plan = 'rsg'.

constraints

A `redist_constr` list of constraints

num_hot_steps

The number of steps to run the simulator at beta = 0. Default is 40000.

num_annealing_steps

The number of steps to run the simulator with linearly changing beta schedule. Default is 60000

num_cold_steps

The number of steps to run the simulator at beta = 1. Default is 20000.

eprob

The probability of keeping an edge connected. The default is 0.05.

lambda

The parameter determining the number of swaps to attempt each iteration of the algorithm. The number of swaps each iteration is equal to Pois(lambda) + 1. The default is 0.

pop_tol

The strength of the hard population constraint. pop_tol = 0.05 means that any proposed swap that brings a district more than 5% away from population parity will be rejected. The default is NULL.

rngseed

Allows the user to set the seed for the simulations. Default is NULL.

maxiterrsg

Maximum number of iterations for random seed-and-grow algorithm to generate starting values. Default is 5000.

adapt_lambda

Whether to adaptively tune the lambda parameter so that the Metropolis-Hastings acceptance probability falls between 20% and 40%. Default is FALSE.

adapt_eprob

Whether to adaptively tune the edgecut probability parameter so that the Metropolis-Hastings acceptance probability falls between 20% and 40%. Default is FALSE.

exact_mh

Whether to use the approximate (0) or exact (1) Metropolis-Hastings ratio calculation for accept-reject rule. Default is FALSE.

savename

Filename to save simulations. Default is NULL.

verbose

Whether to print initialization statement. Default is TRUE.

Value

list of class redist