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finding_estimates.R
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finding_estimates.R
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# ============================================
# Authors: MJ
# Maintainers: MJ
# Copyright: 2023, HRDAG, GPL v2 or later
# ============================================
pacman::p_load(argparse, arrow, dplyr, fs, stringr, here,
digest, glue, readr, purrr, tidyr, assertr,styler)
## 1. Functions to test
# Stratify (no changes)
stratify <- function(replicate_data, schema) {
schema_list <- unlist(str_split(schema, pattern = ","))
grouped_data <- replicate_data %>%
group_by(!!!syms(schema_list)) %>%
select(starts_with("in_"))
stratification_vars <- grouped_data %>%
group_keys() %>%
group_by_all() %>%
group_split()
split_data <- grouped_data %>%
group_split(.keep = FALSE)
return(list(strata_data = split_data,
stratification_vars = stratification_vars))
}
# Lookup_estimates (no changes)
lookup_estimates <- function(stratum_recs, estimates_dir) {
valid_sources <- get_valid_sources(stratum_recs)
lettersplus <- c(letters, paste0(letters, "1"))
stopifnot(length(lettersplus) >= length(valid_sources))
anon_sources <- as.character(glue::glue("in_{lettersplus[1:length(valid_sources)]}"))
stratum_recs <- stratum_recs %>%
select(starts_with("in_"))
options(dplyr.summarise.inform = FALSE)
summary_table <- stratum_recs %>%
dplyr::mutate(rs = rowSums(.)) %>%
dplyr::filter(rs >= 1) %>%
dplyr::select(-rs) %>%
dplyr::mutate(dplyr::across(dplyr::everything(),
~factor(.x, levels = c(0, 1)))) %>%
dplyr::group_by_all() %>%
dplyr::summarize(Freq = n()) %>%
as.data.frame()
if (length(valid_sources) > 0) {
summary_table <- summary_table %>%
dplyr::select(dplyr::all_of(valid_sources), Freq) %>%
purrr::set_names(c(anon_sources, "Freq")) %>%
dplyr::arrange(dplyr::across(dplyr::all_of(anon_sources)))
}
stratum_hash <- digest::digest(summary_table, algo = "sha1")
stratum_dir <- stringr::str_sub(stratum_hash, 1, 2)
estimate_file <- glue::glue("{estimates_dir}/{stratum_dir}/{stratum_hash}.json")
if (file.exists(estimate_file)) {
estimates <- tibble::tibble(N = rjson::fromJSON(file = estimate_file))
return(estimates)
} else {
no_estimates <- tibble::tibble(N = NA_real_)
return(no_estimates)
}
}
# Short_mse (no longer runs lcmcr, just returns a tibble if it doesn't find the estimates)
short_mse <- function(stratum_data, stratum_name,
estimates_dir = NULL, min_n = 1,
K = NULL, buffer_size = 10000, sampler_thinning = 1000, seed = 19481210,
burnin = 10000, n_samples = 10000, posterior_thinning = 500) {
stratum_recs <- stratum_data %>%
dplyr::select(tidyselect::starts_with("in_")) %>%
dplyr::mutate(dplyr::across(tidyselect::everything(), ~if_else(. >= 1, 1, 0)))
valid_sources <- get_valid_sources(stratum_recs, min_n)
if (length(valid_sources) < 3) {
return(tibble::tibble_row(validated = FALSE,
N = NA_real_,
valid_sources = paste(valid_sources, collapse = ","),
n_obs = NA_real_,
stratum_name = stratum_name))
}
# keep only records that appear on one or more valid source (i.e., no rows
# with all 0 values)
stratum_recs <- stratum_recs %>%
dplyr::select(tidyselect::all_of(valid_sources)) %>%
dplyr::mutate(rs = rowSums(.)) %>%
dplyr::filter(rs >= 1) %>%
dplyr::select(-rs)
n_obs <- nrow(stratum_recs)
if (!is.null(estimates_dir)) {
lookup_results <- lookup_estimates(stratum_recs, estimates_dir)
if (all(is.na(lookup_results$N))) {
K <- min((2 ** length(valid_sources)) - 1, 15)
estimates <- tibble::tibble(N = NA_real_)
} else {
estimates <- lookup_results %>%
dplyr::mutate(validated = TRUE,
valid_sources = paste(names(stratum_recs), collapse = ","),
n_obs = n_obs,
stratum_name = stratum_name) %>%
dplyr::select(validated, N, valid_sources, n_obs, stratum_name)
}
}
return(estimates)
}
# mse (function in verdata, no changes)
mse <- function(stratum_data, stratum_name,
estimates_dir = NULL, min_n = 1,
K = NULL, buffer_size = 10000, sampler_thinning = 1000, seed = 19481210,
burnin = 10000, n_samples = 10000, posterior_thinning = 500) {
stratum_recs <- stratum_data %>%
dplyr::select(tidyselect::starts_with("in_")) %>%
dplyr::mutate(dplyr::across(tidyselect::everything(), ~if_else(. >= 1, 1, 0)))
valid_sources <- get_valid_sources(stratum_recs, min_n)
if (length(valid_sources) < 3) {
return(tibble::tibble_row(validated = FALSE,
N = NA_real_,
valid_sources = paste(valid_sources, collapse = ","),
n_obs = NA_real_,
stratum_name = stratum_name))
}
# keep only records that appear on one or more valid source (i.e., no rows
# with all 0 values)
stratum_recs <- stratum_recs %>%
dplyr::select(tidyselect::all_of(valid_sources)) %>%
dplyr::mutate(rs = rowSums(.)) %>%
dplyr::filter(rs >= 1) %>%
dplyr::select(-rs)
n_obs <- nrow(stratum_recs)
if (!is.null(estimates_dir)) {
lookup_results <- lookup_estimates(stratum_recs, estimates_dir)
if (all(is.na(lookup_results$N))) {
# fix model specification to be identical to that used to calculate
# estimates
K <- min((2 ** length(valid_sources)) - 1, 15)
estimates <- run_lcmcr(stratum_recs, stratum_name,
min_n = 1,
K = K,
buffer_size = 10000,
sampler_thinning = 1000,
seed = 19481210,
burnin = 10000,
n_samples = 10000,
posterior_thinning = 500) %>%
dplyr::mutate(validated = TRUE,
n_obs = n_obs) %>%
dplyr::select(validated, N, valid_sources, n_obs, stratum_name)
} else {
estimates <- lookup_results %>%
dplyr::mutate(validated = TRUE,
valid_sources = paste(names(stratum_recs), collapse = ","),
n_obs = n_obs,
stratum_name = stratum_name) %>%
dplyr::select(validated, N, valid_sources, n_obs, stratum_name)
}
} else {
# allow for custom model specification if not looking up existing
# estimates
if (is.null(K)) {
K <- min((2 ** length(valid_sources)) - 1, 15)
}
estimates <- run_lcmcr(stratum_recs, stratum_name, min_n,
K, buffer_size, sampler_thinning, seed,
burnin, n_samples, posterior_thinning) %>%
dplyr::mutate(validated = TRUE,
n_obs = n_obs) %>%
dplyr::select(validated, N, valid_sources, n_obs, stratum_name)
}
return(estimates)
}
## 2. Testing
# 2.1 load data (dowloaded from DANE)
desaparicion <- verdata::read_replicates("~/Documents/verdata-parquet/desaparicion",
"desaparicion", 1, 10) %>%
filter(is_conflict == TRUE) %>%
filter(is_forced_dis == TRUE)
# 2.2 stratification
schema <- ("replica,yy_hecho,is_forced_dis")
estratificacion <- stratify(desaparicion, schema)
# 2.3 Testing that lookup_estimates can find the estimates
test_lookup <- map_dfr(estratificacion$strata_data,
lookup_estimates,
estimates_dir = "~/Documents/estimates")
test_exist <- map_dfr(estratificacion$strata_data,
estimates_exist,
estimates_dir = "~/Documents/estimates")
# 2.4 Testing short_mse
estimaciones <- map2_dfr(.x = estratificacion$strata_data,
.y = estratificacion$stratification_vars,
.f = short_mse,
estimates_dir = "~/Documents/estimates")
# 2.4 Testing mse
estimaciones <- map2_dfr(.x = estratificacion$strata_data,
.y = estratificacion$stratification_vars,
.f = mse,
estimates_dir = "~/Documents/estimates")