redist.flip.anneal: (Deprecated) Flip MCMC Redistricting Simulator using Simulated Annealing

Description

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

Usage

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
)

Value

list of class redist

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.