Searches the local area for a combination of minimizing county splits, compactness, population parity, and keeping close to the original plan
persily(plan, map, counties = NULL)a single plan to optimize from
a redist map object
Required
a redist_plans object with one plan
# \donttest{
data(iowa)
map <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.01, total_pop = pop)
plan <- get_plans_matrix(redist_smc(map, 1))[, 2]
#> SEQUENTIAL MONTE CARLO
#> Sampling 1 99-unit maps with 4 districts and population between 753,973 and 769,205.
#> Split [0/3] ■ | ETA?
#> Split [3/3] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | ETA 0s
#>
local <- persily(plan = plan, map = map, counties = region)
#> FLIP SHORT BURSTS
#> Sampling up to 100 bursts of 50 iterations each.
#> Burst Improve? Score
#> 1 🎇 0.478828
#> 2 🥳 0.494949
#> 9 🪅 0.498660
#> 10 0.498660
#> 20 0.498660
#> 30 0.498660
#> 35 🙂 0.508618
#> 37 😎 0.517074
#> 40 🥂 0.520502
#> 42 ⛄ 0.541098
#> 44 😀 0.572034
#> 50 0.572034
#> 60 0.572034
#> 70 0.572034
#> 80 0.572034
#> 90 0.572034
#> 100 0.572034
# }