redist_flip_anneal
simulates congressional redistricting plans
using Markov chain Monte Carlo methods coupled with simulated annealing.
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
)
redist_plans
A redist_map
object.
The number of samples to draw, not including warmup.
The number of warmup samples to discard.
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.
A redist_constr
object.
The number of steps to run the simulator at beta = 0. Default is 40000.
The number of steps to run the simulator with linearly changing beta schedule. Default is 60000
The number of steps to run the simulator at beta = 1. Default is 20000.
The probability of keeping an edge connected. The
default is 0.05
.
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
.
Whether to adaptively tune the lambda parameter so that the Metropolis-Hastings acceptance probability falls between 20% and 40%. Default is FALSE.
Whether to adaptively tune the edgecut probability parameter so that the Metropolis-Hastings acceptance probability falls between 20% and 40%. Default is FALSE.
Whether to use the approximate (0) or exact (1) Metropolis-Hastings ratio calculation for accept-reject rule. Default is FALSE.
Maximum number of iterations for random seed-and-grow algorithm to generate starting values. Default is 5000.
Whether to print initialization statement.
Default is TRUE
.