Loop through all locations and run GLMM for each

Description

This function runs a mixed-effects generalized linear model (GLMM) for each location within a dataset.

Usage

Repglmre2(data, formula, location_var, random_effect_var, family)

Arguments

Value

A dataframe containing the results

Examples

set.seed(123)

# Create dummy data
  library(dplyr)
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Create HHid (Household ID), grouping every 3-4 records, and convert to character
dummy_data$HHid <- as.character(rep(1:20, each = 5, length.out = nrow(dummy_data)))

# Create a binary outcome variable for years of education
dummy_data$education_binary <- ifelse(dummy_data$years_education > 11, 1, 0)

# Define a logistic regression formula
formula <- education_binary ~ gender_female + household_wealth:gender_female

location_var <- "district_code"
random_effect_var <- "HHid"

# Run the logistic mixed-effects model across all locations (districts)
results <- DHSr::Repglmre2(data = dummy_data, formula = formula,
                           location_var = location_var, random_effect_var = random_effect_var,
                           family = binomial())

# Print the results
print(head(results))

Run Regression Analysis for All Locations

Description

This function runs a regression model for all unique locations within a dataset and combines the results.

Usage

Replm2(
  data,
  formula,
  location_var,
  response_distribution = "normal",
  family = NULL
)

Arguments

Value

A dataframe containing the combined results for all locations.

Examples

set.seed(123)
library(dplyr)
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE) # Represents district codes
) %>% arrange(district_code)

# Define a simple regression formula
formula <- years_education ~ gender_female + household_wealth + household_wealth:gender_female

# Run the regression across all locations (districts)
results1 <- Replm2(dummy_data, formula, "district_code", "normal")
print(results1)

Mixed-Effects Regression Analysis for All Locations

Description

This function runs a mixed-effects regression model for all locations within a dataset.

Usage

Replmre2(data, formula, location_var, random_effect_var)

Arguments

Value

A dataframe containing the results

Examples

set.seed(123)
library(dplyr)
# Create dummy data
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Create HHid (Household ID), grouping every 3-4 records, and convert to character
dummy_data$HHid <- as.character(rep(1:20, each = 5, length.out = nrow(dummy_data)))

# Define a simple regression formula
formula <- years_education ~ gender_female + household_wealth:gender_female
location_var <- "district_code"
random_effect_var <- "HHid"

# Run mixed-effects regression for all districts
results <- DHSr::Replmre2(dummy_data, formula, location_var, random_effect_var)
print(head(results))

Calculate Local Moran's I and Sign Combination Variables

Description

This function calculates Local Moran's I for a specified variable in a dataset and creates sign combination variables based on the standardized variable and the local Moran's I values.

Usage

Spdeplisa(data, variable_name, listw)

Arguments

Value

A data frame containing the original data with additional columns:

Examples

# Load necessary libraries
if (requireNamespace("spData", quietly = TRUE)) {
  library(spData)
  library(sf)
  library(dplyr)

  # Use US states data as a substitute for a shapefile
  us_states <- spData::us_states

  # Simplify for demonstration: Select a subset of columns
  us_states_data <- us_states %>%
    select(GEOID, NAME) %>%
    mutate(mean_wealth = rnorm(nrow(us_states), 50, 10))  # Add mock data

  # Define a temporary shapefile path
  shapefile_path <- tempfile(fileext = ".shp")
  sf::st_write(us_states, shapefile_path, quiet = TRUE)

  # Create spatial weights using the listw function from the package
  us_states_listw <- DHSr::listw(
    shapefile_path = shapefile_path,
    data = us_states_data %>% sf::st_drop_geometry(),  # Drop geometry for compatibility
    loc_shape = "GEOID",
    loc_data = "GEOID",
    weight_function = function(d) exp(-d / 0.2)
  )

  # Apply the Spdeplisa function
  lisa_result <- DHSr::Spdeplisa(
    data = us_states_data,
    variable_name = "mean_wealth",
    listw = us_states_listw
  )

  # View the result
  head(lisa_result)
}

Create Cluster Map Based on Local Moran's I

Description

This function creates a map of clusters based on Local Moran's I values. It identifies clusters using Queen contiguity and visualizes them on a map.

Usage

cluster_map(
  dataset,
  lisa_value,
  lisa_label,
  label,
  lisa_cutoff,
  location_var,
  location_name,
  level2 = NULL,
  id_start = 0,
  comparison = ">",
  min_area = 0,
  min_ = 5,
  title = "Clusters Based on Queen Contiguity",
  subtitle = "",
  footnote = "",
  legend_position = "bottom",
  color_scheme = "C"
)

Arguments

Value

A list with the following components:

Examples

if (requireNamespace("spData", quietly = TRUE)) {
  library(sf)
  library(dplyr)
  library(spdep)
  library(spData)
  library(ggplot2)

  # Load US states data from spData
  us_states <- spData::us_states

  # Simplify for demonstration: Select a subset of columns
  us_states_data <- us_states %>%
    select(GEOID, NAME) %>%
    mutate(mean_wealth = rnorm(nrow(us_states), 50, 10))  # Add mock data

  # Define a shapefile path
  shapefile_path <- tempfile(fileext = ".shp")
  sf::st_write(us_states, shapefile_path, quiet = TRUE)

  # Corrected listw function call using your package
  us_states_listw <- DHSr::listw(
    shapefile_path = shapefile_path,
    data = us_states_data %>% sf::st_drop_geometry(),  # Drop geometry for compatibility
    loc_shape = "GEOID",
    loc_data = "GEOID",
    weight_function = function(d) exp(-d / 0.2)
  )

  # Apply Spdeplisa function
  lisa_result <- DHSr::Spdeplisa(
    data = us_states_data,
    variable_name = "mean_wealth",
    listw = us_states_listw
  )

  # Add LISA labels
  lisa_result <- lisa_result %>%
    mutate(lisa_label = case_when(
      lisa_I > 0 ~ "High-High",
      lisa_I < 0 ~ "Low-Low",
      TRUE ~ "Others"
    ))

  # Apply cluster_map function
  cluster_map_result <- DHSr::cluster_map(
    dataset = lisa_result,
    lisa_value = "lisa_I",
    lisa_label = "lisa_label",
    label = "High-High",
    lisa_cutoff = 0.5,
    location_var = "GEOID",
    location_name = "NAME",
    id_start = 1,
    comparison = ">",
    min_area = 0,
    min_ = 3,  # Reduced for smaller demonstration
    title = "Clusters Based on Queen Contiguity",
    subtitle = "High-High Clusters",
    footnote = "Generated using DHSr package",
    legend_position = "bottom",
    color_scheme = "C"
  )

  # View the resulting dataset with clusters
  head(cluster_map_result$dataset_with_clusters)

  # View the cluster summary
  print(cluster_map_result$summary_clusters)

  # Plot the clusters
  print(cluster_map_result$plot)
}

Create Spatial Weights List

Description

This function creates a spatial weights list using a shapefile and a dataset.

Usage

listw(
  shapefile_path,
  data,
  loc_shape,
  loc_data,
  weight_function = function(d) exp(-d/0.2)
)

Arguments

Value

A spatial weights list object of class 'listw'.

Examples

if (requireNamespace("spData", quietly = TRUE)) {
    library(dplyr)
    library(sf)

    # Load US states data
    us_states <- spData::us_states

    # Simplify for demonstration: Select a subset of columns
    us_states_data <- us_states %>%
        select(GEOID, NAME) %>%
        mutate(mean_wealth = rnorm(nrow(us_states), 50, 10))  # Add mock data

    # Define a temporary shapefile path
    shapefile_path <- tempfile(fileext = ".shp")
    sf::st_write(us_states, shapefile_path, quiet = TRUE)

    # Use the listw function from the package
    us_states_listw <- DHSr::listw(
        shapefile_path = shapefile_path,
        data = us_states_data %>% sf::st_drop_geometry(),  # Drop geometry for compatibility
        loc_shape = "GEOID",
        loc_data = "GEOID",
        weight_function = function(d) exp(-d / 0.2)
    )

    # Verify the spatial weights list
    print(us_states_listw)
}

Mixed-Effects Logistic Regression Analysis for a Specified Location

Description

This function runs a mixed-effects logistic regression model for a specified location within a dataset.

Usage

single_glmre2(
  data,
  formula,
  location_var,
  random_effect_var,
  location_index,
  family = NULL
)

Arguments

Value

The results for sigle location to test

Examples

set.seed(123)
  library(dplyr)
# Create dummy data
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Create HHid (Household ID), grouping every 3-4 records, and convert to character
dummy_data$HHid <- as.character(rep(1:20, each = 5, length.out = nrow(dummy_data)))

# Create a binary outcome variable for years of education
dummy_data$education_binary <- ifelse(dummy_data$years_education > 11, 1, 0)

# Define a logistic regression formula
formula <- education_binary ~ gender_female + household_wealth:gender_female

# Set the location and random effect variables
location_var <- "district_code"
random_effect_var <- "HHid"

# Run the mixed-effects logistic regression for a specific location (e.g., district 1)
result_single_glmre <- single_glmre2(dummy_data, formula, location_var, random_effect_var,
 location_index = 1, family = binomial())

# View the result
print(result_single_glmre)

Linear Regression Analysis for Specified Location

Description

This function runs a linear regression model for a specified location within a dataset.

Usage

single_lm2(
  data,
  formula,
  location_var,
  response_distribution = "normal",
  family = NULL,
  location_index
)

Arguments

Value

A dataframe containing the results for the specified location.

Examples

set.seed(123)
if (requireNamespace("dplyr", quietly = TRUE)) {
  library(dplyr)
# Create dummy data
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Define a simple regression formula
formula <- years_education ~ gender_female + household_wealth + household_wealth:gender_female

# Run the regression for a specific location (e.g., district 1)
result_single_lm <- single_lm2(dummy_data, formula, "district_code",
 response_distribution = "normal", location_index = 1)

# View the result
print(result_single_lm)
}

Mixed-Effects Regression Analysis for Specified Location

Description

This function runs a mixed-effects regression model for a specified location within a dataset.

Usage

single_lmre2(data, formula, location_var, random_effect_var, location_index)

Arguments

Value

the results for the specified location to test

Examples

set.seed(123)
library(dplyr)
# Create dummy data
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Create HHid (Household ID), grouping every 3-4 records, and convert to character
dummy_data$HHid <- as.character(rep(1:20, each = 5, length.out = nrow(dummy_data)))

# Define a simple regression formula
formula <- years_education ~ gender_female + household_wealth:gender_female

# Set the location and random effect variables
location_var <- "district_code"
random_effect_var <- "HHid"

# Run the mixed-effects regression for a specific location (e.g., district 1)
result_single_lmre <- single_lmre2(dummy_data, formula, location_var,
random_effect_var, location_index = 1)

# View the result
print(result_single_lmre)

Calculate Stein's Beta for Each Cluster

Description

This function calculates Stein's Beta for each cluster within the dataset. It applies Stein's shrinkage estimator to the specified beta estimates within each cluster.

Usage

stein_beta(data, cluster_id, beta)

Arguments

Value

A data frame containing the input data with additional columns:

Examples

# Create dummy data
library(dplyr)
set.seed(123)
dummy_data <- data.frame(
  years_education = rnorm(100, 12, 3),    # Represents years of education
  gender_female = rbinom(100, 1, 0.5),    # 1 = Female, 0 = Male
  household_wealth = sample(1:5, 100, replace = TRUE),  # Wealth index from 1 to 5
  district_code = sample(1:10, 100, replace = TRUE)     # Represents district codes
) %>% arrange(district_code)

# Define a regression formula
formula <- years_education ~ gender_female + household_wealth + household_wealth:gender_female

# Run the regression for all districts
results1 <- DHSr::Replm2(dummy_data, formula, "district_code", "normal")

# Assign random clusters for demonstration
clusters <- data.frame(
  district_code = unique(dummy_data$district_code),
  cluster_id = sample(1:3, length(unique(dummy_data$district_code)), replace = TRUE)
)

# Merge clusters with regression results
cluster_beta <- merge(clusters, results1, by.x = "district_code", by.y = "location")

# Apply Stein Beta shrinkage
results_with_stein_beta <- DHSr::stein_beta(
  data = cluster_beta,
  cluster_id = "cluster_id",                # Column for cluster IDs
  beta = "estimate_gender_female"          # Column for beta estimates
)

# View results
print(head(results_with_stein_beta))