`R/redist_flip_tidy.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(
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
)
```

- 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`

.

redist_plans