1d:T79d,# Combine MSE estimation results for a given stratum calculated using multiple replicate files created using multiple imputation. Combination is done using the standard approach that makes use of the laws of total expectation and total variance. ```r combine_estimates(stratum_estimates) ``` ## Arguments - `stratum_estimates`: A data frame of estimates for a stratum of interest calculated using `mse` for all replicates being used for the analysis. The data frame should have columns `N` and `n_obs` from the `mse` function and an additional column `replicate` indicating which replicate the estimates were calculated on. ## Returns A data frame row with the point estimate (`N_mean`) and the associated 95% uncertainty interval (lower bound is `N_025`, upper bound is `N_975`). ## Examples ```r set.seed(19481210) library(dplyr) library(purrr) library(glue) simulate_estimates <- function(stratum_data, replicate_num) { # simulate an imputed stratification variable to determine whether a record # should be considered part of the stratum for estimation stratification_var <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.1, 0.9)) my_stratum <- bind_cols(my_stratum, tibble::tibble(stratification_var)) %>% filter(stratification_var == 1) results <- mse(my_stratum, "my_stratum", K = 4) %>% mutate(replicate = replicate_num) return(results) } in_A <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.45, 0.65)) in_B <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.5, 0.5)) in_C <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(0.75, 0.25)) my_stratum <- tibble::tibble(in_A, in_B, in_C) replicate_nums <- glue("R{1:20}") estimates <- map_dfr(.x = replicate_nums, .f = ~simulate_estimates(stratum_data = my_stratum, replicate_num = .x)) combine_estimates(estimates) ``` ## References \insertRef BDA3verdata1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.

combine_estimates function