redist.findparams is used to find optimal parameter values of
redist_flip for a given map.
Usage
redist.findparams(
map,
nsims,
init_plan = NULL,
adapt_lambda = FALSE,
adapt_eprob = FALSE,
params,
ssdmat = NULL,
group_pop = NULL,
counties = NULL,
nstartval_store = 1,
maxdist_startval = 100,
maxiterrsg = 5000,
report_all = TRUE,
parallel = FALSE,
ncores = NULL,
log = FALSE,
verbose = TRUE
)Arguments
- map
A
redist_mapobject.- nsims
The number of simulations run before a save point.
- init_plan
A vector containing the congressional district labels of each geographic unit. The default is
NULL. If not provided, random and contiguous congressional district assignments will be generated usingredist.rsg.- 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.
- params
A matrix of parameter values to test, such as the output of
expand.grid. Parameters accepted forparamsincludeeprob,lambda,pop_tol,beta, andconstraint.- ssdmat
A matrix of squared distances between geographic units. The default is
NULL.- group_pop
A vector of populations for some sub-group of interest. The default is
NULL.- counties
A vector of county membership assignments. The default is
NULL.- nstartval_store
The number of maps to sample from the preprocessing chain for use as starting values in future simulations. Default is 1.
- maxdist_startval
The maximum distance from the starting map that sampled maps should be. Default is 100 (no restriction).
- maxiterrsg
Maximum number of iterations for random seed-and-grow algorithm to generate starting values. Default is 5000.
- report_all
Whether to report all summary statistics for each set of parameter values. Default is
TRUE.- parallel
Whether to run separate parameter settings in parallel. Default is
FALSE.- ncores
Number of parallel tasks to run, declared outside of the function. Default is
NULL.- log
Whether to open a log to track progress for each parameter combination being tested. Default is FALSE.
- verbose
Whether to print additional information about the tests. Default is
TRUE.
Details
This function allows users to test multiple parameter settings of
redist_flip in preparation for a longer run for analysis.
References
Fifield, Benjamin, Michael Higgins, Kosuke Imai and Alexander Tarr. (2016) "A New Automated Redistricting Simulator Using Markov Chain Monte Carlo." Working Paper. Available at http://imai.princeton.edu/research/files/redist.pdf.
Examples
# \donttest{
data(fl25)
data(fl25_enum)
data(fl25_adj)
## Get an initial partition
init_plan <- fl25_enum$plans[, 5118]
params <- expand.grid(eprob = c(.01, .05, .1))
# Make map
map_fl <- redist_map(fl25, ndists = 3, pop_tol = 0.2)
#> Projecting to CRS 3857
## Run the algorithm
redist.findparams(map_fl,
init_plan = init_plan, nsims = 10000, params = params)
#> ## ------------------------------
#> ## redist.findparams(): Parameter tuning for redist_flip()
#> ## Searching over 3 parameter combinations
#> ## ------------------------------
#>
#>
#> ── redist_flip() ───────────────────────────────────────────────────────────────
#>
#> ── Automated Redistricting Simulation Using Markov Chain Monte Carlo ──
#> ℹ Preprocessing data.
#> ℹ Starting swMH().
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#>
#>
#> ── redist_flip() ───────────────────────────────────────────────────────────────
#>
#> ── Automated Redistricting Simulation Using Markov Chain Monte Carlo ──
#> ℹ Preprocessing data.
#> ℹ Starting swMH().
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#>
#>
#> ── redist_flip() ───────────────────────────────────────────────────────────────
#>
#> ── Automated Redistricting Simulation Using Markov Chain Monte Carlo ──
#> ℹ Preprocessing data.
#> ℹ Starting swMH().
#> ■ 0% | ETA:12s
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#>
#> ## -------------------------------------
#> ## -------------------------------------
#> ## Parameter Values for Simulation 1
#> ## Edgecut probability = 0.01
#> ## Lambda = 0
#> ## No hard population constraint applied
#> ## -------------------------------------
#> ## Diagnostics:
#> ## Metropolis-Hastings Acceptance Ratio = 0.993
#> ## Mean population parity distance = 0.851
#> ## Median population parity distance = 0.828
#> ## Population parity range = 0.029 1.93
#> ## MCMC Iteration quantiles of population parity median = 0.876 0.807 0.876 0.768
#>
#> ## Mean share of geographies equal to initial assignment = 0.352
#> ## Median share of geographies equal to initial assignment = 0.36
#> ## Range of share of geographies equal to initial assignment = 0 0.96
#> ## MCMC Iteration quantiles of geography distance to initial assignment = 0.4 0.4 0.32 0.24
#> ## -------------------------------------
#> ## -------------------------------------
#>
#> ## -------------------------------------
#> ## -------------------------------------
#> ## Parameter Values for Simulation 1
#> ## Edgecut probability = 0.05
#> ## Lambda = 0
#> ## No hard population constraint applied
#> ## -------------------------------------
#> ## Diagnostics:
#> ## Metropolis-Hastings Acceptance Ratio = 0.96
#> ## Mean population parity distance = 0.837
#> ## Median population parity distance = 0.835
#> ## Population parity range = 0.008 1.868
#> ## MCMC Iteration quantiles of population parity median = 0.717 0.963 0.808 0.586
#>
#> ## Mean share of geographies equal to initial assignment = 0.325
#> ## Median share of geographies equal to initial assignment = 0.32
#> ## Range of share of geographies equal to initial assignment = 0 0.96
#> ## MCMC Iteration quantiles of geography distance to initial assignment = 0.36 0.36 0.28 0.28
#> ## -------------------------------------
#> ## -------------------------------------
#>
#> ## -------------------------------------
#> ## -------------------------------------
#> ## Parameter Values for Simulation 1
#> ## Edgecut probability = 0.1
#> ## Lambda = 0
#> ## No hard population constraint applied
#> ## -------------------------------------
#> ## Diagnostics:
#> ## Metropolis-Hastings Acceptance Ratio = 0.923
#> ## Mean population parity distance = 0.899
#> ## Median population parity distance = 0.893
#> ## Population parity range = 0.009 1.946
#> ## MCMC Iteration quantiles of population parity median = 0.875 0.891 0.912 0.889
#>
#> ## Mean share of geographies equal to initial assignment = 0.313
#> ## Median share of geographies equal to initial assignment = 0.28
#> ## Range of share of geographies equal to initial assignment = 0 0.96
#> ## MCMC Iteration quantiles of geography distance to initial assignment = 0.28 0.24 0.32 0.36
#> ## -------------------------------------
#> ## -------------------------------------
#>
#> $diagnostics
#> [1] " ## -------------------------------------\n ## -------------------------------------\n ## Parameter Values for Simulation 1 \n## Edgecut probability = 0.01\n## Lambda = 0\n## No hard population constraint applied\n## -------------------------------------\n## Diagnostics:\n## Metropolis-Hastings Acceptance Ratio = 0.993\n## Mean population parity distance = 0.851\n## Median population parity distance = 0.828\n## Population parity range = 0.029 1.93\n## MCMC Iteration quantiles of population parity median = 0.876 0.807 0.876 0.768\n\n## Mean share of geographies equal to initial assignment = 0.352\n## Median share of geographies equal to initial assignment = 0.36\n## Range of share of geographies equal to initial assignment = 0 0.96\n## MCMC Iteration quantiles of geography distance to initial assignment = 0.4 0.4 0.32 0.24\n## -------------------------------------\n## -------------------------------------\n\n ## -------------------------------------\n ## -------------------------------------\n ## Parameter Values for Simulation 1 \n## Edgecut probability = 0.05\n## Lambda = 0\n## No hard population constraint applied\n## -------------------------------------\n## Diagnostics:\n## Metropolis-Hastings Acceptance Ratio = 0.96\n## Mean population parity distance = 0.837\n## Median population parity distance = 0.835\n## Population parity range = 0.008 1.868\n## MCMC Iteration quantiles of population parity median = 0.717 0.963 0.808 0.586\n\n## Mean share of geographies equal to initial assignment = 0.325\n## Median share of geographies equal to initial assignment = 0.32\n## Range of share of geographies equal to initial assignment = 0 0.96\n## MCMC Iteration quantiles of geography distance to initial assignment = 0.36 0.36 0.28 0.28\n## -------------------------------------\n## -------------------------------------\n\n ## -------------------------------------\n ## -------------------------------------\n ## Parameter Values for Simulation 1 \n## Edgecut probability = 0.1\n## Lambda = 0\n## No hard population constraint applied\n## -------------------------------------\n## Diagnostics:\n## Metropolis-Hastings Acceptance Ratio = 0.923\n## Mean population parity distance = 0.899\n## Median population parity distance = 0.893\n## Population parity range = 0.009 1.946\n## MCMC Iteration quantiles of population parity median = 0.875 0.891 0.912 0.889\n\n## Mean share of geographies equal to initial assignment = 0.313\n## Median share of geographies equal to initial assignment = 0.28\n## Range of share of geographies equal to initial assignment = 0 0.96\n## MCMC Iteration quantiles of geography distance to initial assignment = 0.28 0.24 0.32 0.36\n## -------------------------------------\n## -------------------------------------\n\n"
#>
#> $startvals
#> $startvals[[1]]
#> [,1]
#> [1,] 3
#> [2,] 1
#> [3,] 2
#> [4,] 1
#> [5,] 1
#> [6,] 2
#> [7,] 1
#> [8,] 1
#> [9,] 2
#> [10,] 3
#> [11,] 2
#> [12,] 2
#> [13,] 2
#> [14,] 2
#> [15,] 2
#> [16,] 2
#> [17,] 2
#> [18,] 2
#> [19,] 1
#> [20,] 1
#> [21,] 2
#> [22,] 1
#> [23,] 2
#> [24,] 1
#> [25,] 1
#>
#> $startvals[[2]]
#> [,1]
#> [1,] 2
#> [2,] 2
#> [3,] 2
#> [4,] 2
#> [5,] 2
#> [6,] 3
#> [7,] 2
#> [8,] 2
#> [9,] 1
#> [10,] 2
#> [11,] 2
#> [12,] 2
#> [13,] 2
#> [14,] 2
#> [15,] 2
#> [16,] 2
#> [17,] 2
#> [18,] 2
#> [19,] 2
#> [20,] 2
#> [21,] 2
#> [22,] 2
#> [23,] 3
#> [24,] 2
#> [25,] 2
#>
#> $startvals[[3]]
#> [,1]
#> [1,] 1
#> [2,] 3
#> [3,] 3
#> [4,] 3
#> [5,] 1
#> [6,] 1
#> [7,] 1
#> [8,] 1
#> [9,] 1
#> [10,] 2
#> [11,] 2
#> [12,] 2
#> [13,] 2
#> [14,] 1
#> [15,] 2
#> [16,] 3
#> [17,] 3
#> [18,] 1
#> [19,] 3
#> [20,] 3
#> [21,] 1
#> [22,] 3
#> [23,] 1
#> [24,] 3
#> [25,] 3
#>
#>
# }