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Flip MCMC Redistricting Simulator using Simulated Annealing

Source: R/redist_flip.R
redist_flip_anneal.Rd

redist_flip_anneal simulates congressional redistricting plans using Markov chain Monte Carlo methods coupled with simulated annealing.

Usage

redist_flip_anneal(
  map,
  nsims,
  warmup = 0,
  init_plan = NULL,
  constraints = redist_constr(),
  num_hot_steps = 40000,
  num_annealing_steps = 60000,
  num_cold_steps = 20000,
  eprob = 0.05,
  lambda = 0,
  adapt_lambda = FALSE,
  adapt_eprob = FALSE,
  exact_mh = FALSE,
  maxiterrsg = 5000,
  verbose = TRUE
)

Arguments

map

A redist_map object.

nsims

The number of samples to draw, not including warmup.

warmup

The number of warmup samples to discard.

init_plan

A vector containing the congressional district labels of each geographic unit. The default is NULL. If not provided, a random initial plan will be generated using redist_smc. You can also request to initialize using redist.rsg by supplying 'rsg', though this is not recommended behavior.

constraints

A redist_constr object.

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.

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.

maxiterrsg

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

verbose

Whether to print initialization statement. Default is TRUE.

Value

redist_plans