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Figure5C.Rmd
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---
title: "mediation_for_behavior"
output: html_document
---
```{r setup, include=FALSE}
library(mediation)
library(forcats)
library(ggplot2)
CBG_data <- read.csv("data/for_mediation.csv", header = TRUE, row.names = NULL)
CBG_data$Race <- as.factor(CBG_data$Race)
CBG_data$Race <- fct_relevel(CBG_data$Race, "white")
CBG_data$population <- CBG_data[, 'weight']
CBG_data$housing_value_orig <- CBG_data$housing_value / 1000000
```
```{r}
columns_to_check <- c('Race', 'reach', 'gyr')
for_regress <- subset(CBG_data, complete.cases(CBG_data[, columns_to_check]))
model <- lm(gyr ~ reach + Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
mediator_model <- lm(reach ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
total_model <- lm(gyr ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
b <- model$coefficients['reach']
cpr <- model$coefficients['Raceblack']
a <- mediator_model$coefficients['Raceblack']
c0 <-total_model$coefficients['Raceblack']
c(c0, a, cpr, b)
mediation_result <- mediate(mediator_model, model, sims = 5000, treat='Race', mediator = 'reach', control.value = 'white', treat.value = 'black')
summary(mediation_result)
mediation_result$d0
mediation_result$d0.ci
mediation_result$z0
mediation_result$z0.ci
mediation_result$tau.coef
mediation_result$tau.ci
mediation_result$n0
mediation_result$n0.ci
```
```{r}
columns_to_check <- c('Race', 'job', 'unemployed_ratio')
for_regress <- subset(CBG_data, complete.cases(CBG_data[, columns_to_check]))
model <- lm(unemployed_ratio ~ job + Race+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
mediator_model <- lm(job ~ Race+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
total_model <- lm(unemployed_ratio ~Race+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
b <- model$coefficients['job']
cpr <- model$coefficients['Raceblack']
a <- mediator_model$coefficients['Raceblack']
c0 <-total_model$coefficients['Raceblack']
c(c0, a, cpr, b)
mediation_result <- mediate(mediator_model, model, sims = 5000, treat='Race', mediator = 'job', control.value = 'white', treat.value = 'black')
summary(mediation_result)
mediation_result$d0
mediation_result$d0.ci
mediation_result$z0
mediation_result$z0.ci
mediation_result$tau.coef
mediation_result$tau.ci
mediation_result$n0
mediation_result$n0.ci
```
```{r}
columns_to_check <- c('Race', 'health', 'health_dist')
for_regress <- subset(CBG_data, complete.cases(CBG_data[, columns_to_check]))
model <- lm(health_dist ~ health+Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
mediator_model <- lm(health ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
total_model <- lm(health_dist ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
b <- model$coefficients['health']
cpr <- model$coefficients['Raceblack']
a <- mediator_model$coefficients['Raceblack']
c0 <-total_model$coefficients['Raceblack']
c(c0, a, cpr, b)
mediation_result <- mediate(mediator_model, model, sims = 5000, treat='Race', mediator = 'health', control.value = 'white', treat.value = 'black')
summary(mediation_result)
mediation_result$d0
mediation_result$d0.ci
mediation_result$z0
mediation_result$z0.ci
mediation_result$tau.coef
mediation_result$tau.ci
mediation_result$n0
mediation_result$n0.ci
```
```{r}
columns_to_check <- c('Race', 'park', 'park_dist')
for_regress <- subset(CBG_data, complete.cases(CBG_data[, columns_to_check]))
model <- lm(park_dist ~ park+Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
mediator_model <- lm(park ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
total_model <- lm(park_dist ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
b <- model$coefficients['park']
cpr <- model$coefficients['Raceblack']
a <- mediator_model$coefficients['Raceblack']
c0 <-total_model$coefficients['Raceblack']
c(c0, a, cpr, b)
mediation_result <- mediate(mediator_model, model, sims = 5000, treat='Race', mediator = 'park', control.value = 'white', treat.value = 'black')
summary(mediation_result)
mediation_result$d0
mediation_result$d0.ci
mediation_result$z0
mediation_result$z0.ci
mediation_result$tau.coef
mediation_result$tau.ci
mediation_result$n0
mediation_result$n0.ci
```
```{r}
columns_to_check <- c('Race', 'school', 'school_dist')
for_regress <- subset(CBG_data, complete.cases(CBG_data[, columns_to_check]))
model <- lm(school_dist ~ school+Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
mediator_model <- lm(school ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
total_model <- lm(school_dist ~ Race+unemployed_ratio+foreign_born_ratio+income_percentile+vehicle_rate+public_transit_ratio+poverty_ratio+couple_parent_children+housing_value_orig, data = for_regress, weights = population)
b <- model$coefficients['school']
cpr <- model$coefficients['Raceblack']
a <- mediator_model$coefficients['Raceblack']
c0 <-total_model$coefficients['Raceblack']
c(c0, a, cpr, b)
mediation_result <- mediate(mediator_model, model, sims = 5000, treat='Race', mediator = 'school', control.value = 'white', treat.value = 'black')
summary(mediation_result)
mediation_result$d0
mediation_result$d0.ci
mediation_result$z0
mediation_result$z0.ci
mediation_result$tau.coef
mediation_result$tau.ci
mediation_result$n0
mediation_result$n0.ci
```