Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

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.

Description

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.

Usage

combine_estimates(stratum_estimates)

Arguments

Value

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).

References

Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013). Bayesian Data Analysis, 0 edition. Chapman and Hall/CRC. ISBN 978-0-429-11307-9, doi:10.1201/b16018.

Examples

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)

Combine imputed replicates according to calculate totals. Combination is done using the standard approach that makes use of the laws of total expectation and total variance.

Description

Combine imputed replicates according to calculate totals. Combination is done using the standard approach that makes use of the laws of total expectation and total variance.

Usage

combine_replicates(
  violation,
  replicates_obs_data,
  replicates_data,
  strata_vars = NULL,
  conflict_filter = TRUE,
  forced_dis_filter = FALSE,
  edad_minors_filter = FALSE,
  include_props = FALSE,
  digits = 2
)

Arguments

Value

A data frame with 5 or more columns: name of variable(s), observed the number of observations in each category for every variable, imp_lo the lower bound of the 95% confidence interval, imp_hi the upper bound of the 95% confidence interval, and imp_mean the point estimate of the mean value.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2),
version = "v1")
replicates_obs_data <- summary_observed("reclutamiento", replicates_data,
strata_vars = "sexo", conflict_filter = FALSE, forced_dis_filter = FALSE,
edad_minors_filter = FALSE, include_props = FALSE, digits = 2)
tab_combine <- combine_replicates("reclutamiento", replicates_obs_data,
replicates_data, strata_vars = 'sexo', conflict_filter = TRUE,
forced_dis_filter = FALSE, edad_minors_filter = FALSE, include_props = FALSE,
digits = 2)

Confirm files are identical to the ones published.

Description

Confirm files are identical to the ones published.

Usage

confirm_files(replicates_dir, violation, replicate_nums, version)

Arguments

Value

A data frame row with replicate_num rows and two columns: replicate_path, a string indicating the path to the replicate checked and confirmed, a boolean values indicating whether the replicate contents match the published version.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
confirm_files(local_dir, "reclutamiento", c(1, 2), version = "v1")

Diccionario de datos para las variables que aparecen en los archivos de las réplicas.

Description

Diccionario de datos para las variables que aparecen en los archivos de las réplicas.

Usage

data(diccionario_replicas)

Format

Un data frame con 55 filas y 4 variables.

nombre_variable

nombre de la variable

tipo

tipo de la variable: caracter, numérico, lógico

detalle_variable

explicación detallada de la variable

categorias_variable

valores posibles de la variable

Source

Proyecto conjunto JEP-CEV-HRDAG.

Variables adicionales que pueden ser útiles para analizar los datos.

Description

Variables adicionales que pueden ser útiles para analizar los datos.

Usage

data(diccionario_vars_adicional)

Format

Un data frame con 11 filas y 4 variables.

nombre_variable

nombre de la variable

tipo

tipo de la variable: caracter, numérico, lógico

detalle_variable

explicación detallada de la variable

categorias_variable

valores posibles de la variable

Source

Proyecto conjunto JEP-CEV-HRDAG.

Check whether stratum estimates already exist in pre-calculated files.

Description

Check whether stratum estimates already exist in pre-calculated files.

Usage

estimates_exist(stratum_data_prepped, estimates_dir)

Arguments

Value

A list with two entries, estimates_exist and estimates_path. estimates_exist is a logical value indicating whether calculations for the stratum of interest are available in the directory containing the pre-calculated estimates. If estimates_exist is TRUE, estimates_path will contain the full file path to the JSON file containing the estimates, otherwise it will be NA.

Examples

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))
in_D <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(1, 0))

my_stratum <- tibble::tibble(in_A, in_B, in_C, in_D) %>%
    dplyr::mutate(rs = rowSums(.)) %>%
    dplyr::filter(rs >= 1) %>%
    dplyr::select(-rs)

estimates_exist(stratum_data_prepped = my_stratum, estimates_dir = "path_to_estimates")

Datos que documentan las estratificaciones necesarias para replicar los resultados del informe metodológico del proyecto conjunto CEV-HRDAG-JEP (versión en español).

Description

Datos que documentan las estratificaciones necesarias para replicar los resultados del informe metodológico del proyecto conjunto CEV-HRDAG-JEP (versión en español).

Usage

data(estratificacion)

Format

Un data frame con 31 filas y 4 variables.

violacion

el hecho de violencia al analizar

estimacion

el tipo de análisis que utiliza la estratificación (p.ej., patrones de violencia por año, sexo, etc.)

estratificacion

las variables utilizadas para estratificar las estimaciones

notas

notas adicionales sobre la estratificación; NA si no hay notas

Source

Proyecto conjunto JEP-CEV-HRDAG.

Filter records to replicate results presented in the CEV methodology report.

Description

Filter records to replicate results presented in the CEV methodology report.

Usage

filter_standard_cev(replicates_data, violation, perp_change = TRUE)

Arguments

Value

A filtered data frame.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2), version = "v1")
filter_standard_cev(replicates_data, "reclutamiento", perp_change = TRUE)

Determine valid sources for estimation of a stratum of interest.

Description

Determine valid sources for estimation of a stratum of interest.

Usage

get_valid_sources(stratum_data_prepped, min_n = 1)

Arguments

Value

A character vector containing the names of the valid sources.

Examples

set.seed(19481210)
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))
in_D <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(1, 0))

my_stratum <- tibble::tibble(in_A, in_B, in_C, in_D)
get_valid_sources(my_stratum)

lookup_estimates

Description

Look up and read in existing estimates from pre-calculated files.

Usage

lookup_estimates(stratum_data_prepped, estimates_dir)

Arguments

Value

A data frame with one column, N, indicating the results. If the stratum was not found in the pre-calculated files, N will be NA and the data frame will have one row. If the stratum was found in the pre-calculated files, N will contain draws from the posterior distribution of the model and the data frame will contain 1,000 rows.

Examples

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))
in_D <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(1, 0))

my_stratum <- tibble::tibble(in_A, in_B, in_C, in_D) %>%
    dplyr::mutate(rs = rowSums(.)) %>%
    dplyr::filter(rs >= 1) %>%
    dplyr::select(-rs)

lookup_estimates(stratum_data_prepped = my_stratum, estimates_dir = "path_to_estimates")

mse

Description

Prepare data for estimation and calculate estimates using run_lcmcr.

Usage

mse(
  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
)

Arguments

Value

A data frame with five columns. validated is a logical value indicating whether the stratum is estimable, N is the draws from the posterior distribution (NA if the stratum is not estimable), valid_sources is a string indicating which sources were used in the estimation, n_obs is the number of observations on valid lists in the stratum of interest (NA if the stratum is not estimable), and stratum_name is a stratum identifier. If the stratum is estimable the return will consist of n_samples divided by 1,000 rows.

Examples

set.seed(19481210)
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))
in_D <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(1, 0))

my_stratum <- tibble::tibble(in_A, in_B, in_C, in_D)
mse(stratum_data = my_stratum, stratum_name = "my_stratum")

Calculate the proportions of each level of a variable after applying combine_replicates to completed data (includes imputed values).

Description

Calculate the proportions of each level of a variable after applying combine_replicates to completed data (includes imputed values).

Usage

proportions_imputed(complete_data, strata_vars, digits = 2)

Arguments

Value

A data frame that contains the proportions after applying combine_replicates.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
replicates_data <- read_replicates(replicates_dir = local_dir,
violation = "reclutamiento", replicate_nums = c(1, 2), version = "v1",
crash = TRUE)
replicates_obs_data <- summary_observed("reclutamiento", replicates_data,
strata_vars = "sexo", conflict_filter = FALSE, forced_dis_filter = FALSE,
edad_minors_filter = FALSE, include_props = FALSE)
tab_combine <- combine_replicates("reclutamiento", replicates_obs_data,
replicates_data, strata_vars = 'sexo', conflict_filter = TRUE,
forced_dis_filter = FALSE, edad_minors_filter = FALSE, include_props = FALSE)
prop_data_complete <- proportions_imputed(tab_combine, strata_vars = "sexo",
digits = 2)

Calculate the proportions of each level of a variable after applying summary_observed to observed values.

Description

Calculate the proportions of each level of a variable after applying summary_observed to observed values.

Usage

proportions_observed(obs_data, strata_vars, digits = 2)

Arguments

Value

A data frame that contains the proportions after applying summary_observed.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2), version = "v1")
tab_observed <- summary_observed("reclutamiento", replicates_data,
strata_vars = "sexo", conflict_filter = TRUE, forced_dis_filter = FALSE,
edad_minors_filter = TRUE, include_props = TRUE)
prop_data <- proportions_observed(tab_observed, strata_vars = "sexo",
digits = 2)

Read replicates in a directory and verify they are identical to the ones published.

Description

Read replicates in a directory and verify they are identical to the ones published.

Usage

read_replicates(
  replicates_dir,
  violation,
  replicate_nums,
  version,
  crash = TRUE
)

Arguments

Value

A data frame with the data from all indicated replicates.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
read_replicates(local_dir, "reclutamiento", 1, 2, version = "v1")

Calculate multiple systems estimation estimates using the Bayesian Non-Parametric Latent-Class Capture-Recapture model developed by Daniel Manrique-Vallier (2016).

Description

Calculate multiple systems estimation estimates using the Bayesian Non-Parametric Latent-Class Capture-Recapture model developed by Daniel Manrique-Vallier (2016).

Usage

run_lcmcr(
  stratum_data_prepped,
  stratum_name,
  min_n = 1,
  K,
  buffer_size,
  sampler_thinning,
  seed,
  burnin,
  n_samples,
  posterior_thinning
)

Arguments

Value

A data frame with four columns and n_samples divided by 1,000 rows. N is the draws from the posterior distribution, valid_sources is a string indicating which sources were used in the estimation, n_obs is the number of observations in the stratum of interest, and stratum_name is the stratum identifier.

References

Manrique‐Vallier D (2016). “Bayesian population size estimation using Dirichlet process mixtures.” Biometrics, 72(4), 1246–1254. doi:10.1111/biom.12502.

Examples

set.seed(19481210)
library(dplyr)

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))
in_D <- sample(c(0, 1), size = 100, replace = TRUE, prob = c(1, 0))

my_stratum <- tibble::tibble(in_A, in_B, in_C, in_D) %>%
    dplyr::mutate(rs = rowSums(.)) %>%
    dplyr::filter(rs >= 1) %>%
    dplyr::select(-rs)
run_lcmcr(stratum_data_prepped = my_stratum, stratum_name = "my_stratum",
          K = 4, buffer_size = 10000, sampler_thinning = 1000, seed = 19481210,
          burnin = 10000, n_samples = 10000, posterior_thinning = 500)

Data documenting the stratifications used to replicate the results of the methodological report of the joint JEP-CEV-HRDAG project (version in English).

Description

Data documenting the stratifications used to replicate the results of the methodological report of the joint JEP-CEV-HRDAG project (version in English).

Usage

data(stratification)

Format

A data frame with 31 rows and 4 variables.

violation

the human rights violation being analyzed

estimation

the type of analysis the stratification was used for (e.g., patterns of violence by year, sex, etc.)

stratification

the variables used to stratify the estimates

notes

additional notes about the stratification; NA if no notes

Source

Joint JEP-CEV-HRDAG project.

Summary statistics for observed data.

Description

Summary statistics for observed data.

Usage

summary_observed(
  violation,
  replicates_data,
  strata_vars = NULL,
  conflict_filter = FALSE,
  forced_dis_filter = FALSE,
  edad_minors_filter = FALSE,
  include_props = FALSE,
  digits = 2
)

Arguments

Value

A data frame with two or more columns, (1) name of variable(s) and (2) the number of observations in each of the variable's categories.

Examples

local_dir <- system.file("extdata", "right", package = "verdata")
replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2), version = "v1")
tab_observed <- summary_observed("reclutamiento", replicates_data,
strata_vars = "sexo", conflict_filter = FALSE, forced_dis_filter = FALSE,
edad_minors_filter = FALSE, include_props = FALSE, digits = 2)