`R/redist_mcmc.R`

`redist.flip.anneal.Rd`

`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
)
```

- 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`

.

list of class redist