Prints diagnostic information, which varies by algorithm. All algorithms
plans_diversity() of the samples.
# S3 method for redist_plans summary(object, district = 1L, all_runs = TRUE, vi_max = 100, ...)
a redist_plans object
When there are multiple SMC runs, show detailed summary statistics for all runs (the default), or only the first run?
The maximum number of plans to sample in computing the pairwise variation of information distance (sample diversity).
additional arguments (ignored)
A data frame containing diagnostic information, invisibly.
For SMC and MCMC, if there are multiple runs/chains, R-hat values will be computed for each summary statistic. These values should be close to 1. If they are not, then there is too much between-chain variation, indicating that there are not enough samples. R-hat values are calculated after rank-normalization and folding. MCMC chains are split in half before R-hat is computed. For summary statistics that vary across districts, R-hat is calculated for the first district only.
For SMC, diagnostics statistics include:
Effective samples: the effective sample size at each iteration, computed using the SMC weights. Larger is better. The percentage in parentheses is the ratio of the effective samples to the total samples.
Acceptance rate: the fractino of drawn spanning trees which yield a valid redistricting plan within the population tolerance. Very small values (< 1%) can indicate a bottleneck and may lead to a lack of diversity.
Standard deviation of the log weights: More variable weights (larger s.d.) indicate less efficient sampling. Values greater than 3 are likely problematic.
Maximum unique plans: an upper bound on the number of unique redistricting plans that survive each stage. The percentage in parentheses is the ratio of this number to expected number of unique plans under equal-probability multinomial resampling. Small values (< 100) indicate a bottleneck, which leads to a loss of sample diversity and a higher variance.
k parameter: How many spanning tree edges were considered for
cutting at each split. Mostly informational, though large jumps may indicate
a need to increase
Bottleneck: An asterisk will appear in the right column if a bottleneck appears likely, based on the values of the other statistics.
In the event of problematic diagnostics, the function will provide suggestions for improvement.
data(iowa) iowa_map <- redist_map(iowa, ndists = 4, pop_tol = 0.1) plans <- redist_smc(iowa_map, 100) #> SEQUENTIAL MONTE CARLO #> Sampling 100 99-unit maps with 4 districts and population between 685,430 and 837,748. #> Split [0/3] ■ | ETA? #> Split [3/3] ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ | ETA 0s #> summary(plans) #> SMC: 100 sampled plans of 4 districts on 99 units #> `adapt_k_thresh`=0.985 • `seq_alpha`=0.5 #> `est_label_mult`=1 • `pop_temper`=0 #> #> Plan diversity 80% range: 0.47 to 0.81 #> #> Sampling diagnostics for SMC run 1 of 1 (100 samples) #> Eff. samples (%) Acc. rate Log wgt. sd Max. unique Est. k #> Split 1 97 (97.4%) 20.6% 0.32 61 ( 97%) 14 #> Split 2 96 (96.0%) 39.5% 0.40 59 ( 93%) 8 #> Split 3 97 (96.6%) 10.7% 0.37 57 ( 90%) 7 #> Resample 88 (87.8%) NA% 0.39 62 ( 98%) NA #> #> • Watch out for low effective samples, very low acceptance rates (less than 1%), large std. devs. of the log weights #> (more than 3 or so), and low numbers of unique plans. R-hat values for summary statistics should be between 1 and 1.05.