Builds a confidence interval for a quantity of interest. If multiple runs are available, uses the between-run variation to estimate the standard error. If only one run is available, uses information on the SMC particle/plan genealogy to estimate the standard error, using a variant of the method of Olson & Douc (2019). The multiple-run estimator is more reliable, especially for situations with many districts, and should be used when parallelism is available. All reference plans are ignored.
redist_smc_ci(plans, x, district = 1L, conf = 0.9)
A tibble with three columns:
X is the name of the vector of interest,
containing the mean and confidence interval. When used inside
summarize() this will create three columns in the
Lee, A., & Whiteley, N. (2018). Variance estimation in the particle filter. Biometrika, 105(3), 609-625. Olsson, J., & Douc, R. (2019). Numerically stable online estimation of variance in particle filters. Bernoulli, 25(2), 1504-1535. H. P. Chan and T. L. Lai. A general theory of particle filters in hidden Markov models and some applications. Ann. Statist., 41(6):2877–2904, 2013.
library(dplyr) data(iowa) iowa_map <- redist_map(iowa, existing_plan = cd_2010, pop_tol = 0.05) plans <- redist_mergesplit_parallel(iowa_map, nsims = 200, chains = 2, silent = TRUE) %>% mutate(dem = group_frac(iowa_map, dem_08, dem_08 + rep_08)) %>% number_by(dem) redist_smc_ci(plans, dem) #> Warning: Runs have not converged for this statistic. #> ℹ R-hat is 1.153 #> → Increase the number of samples. #> # A tibble: 1 × 3 #> dem dem_lower dem_upper #> <dbl> <dbl> <dbl> #> 1 0.464 0.453 0.476