redist.mcmc.anneal simulates congressional redistricting plans
using Markov chain Monte Carlo methods coupled with simulated annealing.
redist.mcmc.anneal( adjobj, popvec, ndists = NULL, initcds = NULL, num_hot_steps = 40000, num_annealing_steps = 60000, num_cold_steps = 20000, eprob = 0.05, lambda = 0, popcons = NULL, grouppopvec = NULL, areasvec = NULL, countymembership = NULL, borderlength_mat = NULL, ssdmat = NULL, constraint = NULL, constraintweights = NULL, compactness_metric = "fryer-holden", rngseed = NULL, maxiterrsg = 5000, adapt_lambda = FALSE, adapt_eprob = FALSE, contiguitymap = "rooks", exact_mh = FALSE, savename = NULL, verbose = TRUE, ncores = 1, tgt_min = 0.55, tgt_other = 0.25 )
An adjacency matrix, list, or object of class "SpatialPolygonsDataFrame."
A vector containing the populations of each geographic unit
The numbe of congressional districts. The default is
A vector containing the congressional district labels
of each geographic unit. The default is
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
The parameter detmerining the number of swaps to attempt
each iteration fo the algoirhtm. The number of swaps each iteration is
equal to Pois(
The strength of the hard population
A vector of populations for some sub-group of
interest. The default is
A vector of precinct areas for discrete Polsby-Popper.
The default is
A vector of county membership assignments. The default is
A matrix of border length distances, where
the first two columns are the indices of precincts sharing a border and
the third column is its distance. Default is
A matrix of squared distances between geographic
units. The default is
Which constraint to apply. Accepts any combination of
The weights to apply to each constraint. Should be a vector the same length as constraint. Default is NULL.
The compactness metric to use when constraining on
compactness. Default is
Allows the user to set the seed for the
simulations. Default is
Maximum number of iterations for random seed-and-grow algorithm to generate starting values. Default is 5000.
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.
Use queens or rooks distance criteria for generating an adjacency list from a "SpatialPolygonsDataFrame" data type. Default is "rooks".
Whether to use the approximate (0) or exact (1) Metropolis-Hastings ratio calculation for accept-reject rule. Default is FALSE.
Filename to save simulations. Default is
Whether to print initialization statement.
The number of cores available to parallelize over. Default is 1.
The majority minority target percent as a decimal. Default is 0.55.
The remaining target percent as a decimal. Default is 0.25.