|
|
--- |
|
|
title: "Study2 (Appendix)" |
|
|
author: "Yuya Endo" |
|
|
date: "2020-11-12" |
|
|
output: |
|
|
html_document: |
|
|
theme: cerulean |
|
|
highlight: tango |
|
|
toc: true |
|
|
toc_depth: 3 |
|
|
toc_float: true |
|
|
self_contained: true |
|
|
--- |
|
|
|
|
|
``` |
|
|
|
|
|
R version 4.0.2 (2020-06-22) |
|
|
Platform: x86_64-apple-darwin17.0 (64-bit) |
|
|
Running under: macOS Catalina 10.15.4 |
|
|
|
|
|
Matrix products: default |
|
|
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib |
|
|
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib |
|
|
|
|
|
locale: |
|
|
ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8 |
|
|
|
|
|
attached base packages: |
|
|
grid stats graphics grDevices utils datasets methods |
|
|
base |
|
|
|
|
|
other attached packages: |
|
|
patchwork_1.0.0 ggstance_0.3.4 cjoint_2.1.0 survey_4.0 |
|
|
survival_3.1-12 Matrix_1.2-18 lmtest_0.9-37 zoo_1.8-7 |
|
|
sandwich_2.5-1 gt_0.2.0.5 forcats_0.5.0 stringr_1.4.0 |
|
|
dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 |
|
|
tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0 |
|
|
``` |
|
|
|
|
|
```{r, include = FALSE} |
|
|
knitr::opts_chunk$set( |
|
|
warning = FALSE, |
|
|
message = FALSE, |
|
|
comment = "", |
|
|
fig.align = "center" |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r road pkg} |
|
|
library(tidyverse) |
|
|
library(gt) |
|
|
library(cjoint) |
|
|
library(ggstance) |
|
|
library(patchwork) |
|
|
``` |
|
|
|
|
|
```{r read data} |
|
|
df_type1 <- read_csv("data/study2_data_type1.csv") |
|
|
df_type2 <- read_csv("data/study2_data_type2.csv") |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_conjoint} |
|
|
df_conjoint <- bind_rows(df_type1, df_type2) %>% |
|
|
mutate(across(where(is.character), as.factor)) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r df_samp_freq} |
|
|
df_samp_freq <- df_type2 %>% |
|
|
select(Age = agegroup, Race = R_race, Hispanic, Male) %>% |
|
|
mutate( |
|
|
Ethnicity = if_else(Hispanic == 1, "Hispanic", "Non-Hispanic"), |
|
|
Gender = if_else(Male == 1, "Male", "Female") |
|
|
) %>% |
|
|
select(Age, Race, Ethnicity, Gender) %>% |
|
|
pivot_longer(Age:Gender, |
|
|
names_to = "variable", values_to = "value") %>% |
|
|
group_by(variable) %>% |
|
|
count(value) %>% |
|
|
mutate( |
|
|
freq = n/sum(n)*100, |
|
|
variable = factor(variable, levels = c("Age", "Race", |
|
|
"Ethnicity", "Gender")) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r tb_samp_freq} |
|
|
tb_samp_freq <- df_samp_freq %>% |
|
|
select(variable, value, freq) %>% |
|
|
mutate(freq = round(freq, 2)) %>% |
|
|
rename(Characteristic = value, `Proportion of Sample` = freq) %>% |
|
|
mutate(Characteristic = fct_relevel(Characteristic, |
|
|
c("18-25", "26-35", "36-45", "46-55", |
|
|
"56-65", "66 or older", "White", |
|
|
"Black", "Hispanic", "Asian", |
|
|
"Other", "Hispanic", "Non-Hispanic", |
|
|
"Male", "Female"))) %>% |
|
|
arrange(Characteristic) %>% |
|
|
gt(rowname_col = "row", |
|
|
groupname_col = "variable") %>% |
|
|
cols_label(`Proportion of Sample` = html("Proportion of <br> Sample")) %>% |
|
|
row_group_order(groups = c("Age", "Race", "Ethnicity", "Gender")) |
|
|
|
|
|
tb_samp_freq |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r attribute_list} |
|
|
attribute_list <- list() |
|
|
attribute_list[["Sex"]] <- c("Male", "Female") |
|
|
attribute_list[["Age"]] <- c("44 years old","52 years old","60 years old", |
|
|
"68 years old","76 years old") |
|
|
attribute_list[["Race/Ethnicity"]] <- c("White","Black", |
|
|
"Hispanic","Asian American") |
|
|
attribute_list[["Marital status"]] <- c("Single","Married") |
|
|
attribute_list[["Parental status"]] <- c("No children","1 child","2 children") |
|
|
attribute_list[["Experience in legal profession"]] <- c("No experience", |
|
|
"5 years", |
|
|
"10 years", |
|
|
"15 years", |
|
|
"20 years") |
|
|
attribute_list[["Law school ranking"]] <- c("Top 10 (Tier 1)", |
|
|
"50-100 (Tier 2)","151-200 (Tier 4)") |
|
|
attribute_list[["Party affiliation"]] <- c("Democratic Party","Republican Party") |
|
|
``` |
|
|
|
|
|
|
|
|
```{r conjoint_design} |
|
|
conjoint_design <- makeDesign(type = "constraints", |
|
|
attribute.levels = attribute_list) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r baselines} |
|
|
baselines <- list() |
|
|
baselines[["Sex"]] <- c("Male") |
|
|
baselines[["Age"]] <- c("44 years old") |
|
|
baselines[["Race/Ethnicity"]] <- c("White") |
|
|
baselines[["Marital status"]] <- c("Single") |
|
|
baselines[["Parental status"]] <- c("No children") |
|
|
baselines[["Experience in legal profession"]] <- c("No experience") |
|
|
baselines[["Law school ranking"]] <- c("Top 10 (Tier 1)") |
|
|
baselines[["Party affiliation"]] <- c("Democratic Party") |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r acie_partisan} |
|
|
acie_partisan <- df_conjoint %>% |
|
|
drop_na(Partisanship) %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ (Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`) * Partisanship, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines, |
|
|
respondent.varying = c("Partisanship")) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r base_level function} |
|
|
base_level <- function(data, mod){ |
|
|
df_base_pa <- summary(mod)$baselines_amce %>% |
|
|
mutate( |
|
|
Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")") |
|
|
) |
|
|
pa <- data %>% |
|
|
mutate( |
|
|
lwr = Estimate - 1.96 * `Std. Err`, |
|
|
upr = Estimate + 1.96 * `Std. Err` |
|
|
) %>% |
|
|
bind_rows(df_base_pa) %>% |
|
|
mutate( |
|
|
Level = as.factor(Level), |
|
|
Level = factor(Level, |
|
|
levels = c("Republican Party", |
|
|
"Party affiliation:\n(Baseline = Democratic Party)", |
|
|
"151-200 (Tier 4)", |
|
|
"50-100 (Tier 2)", |
|
|
"Law school ranking:\n(Baseline = Top 10 (Tier 1))", |
|
|
"20 years", |
|
|
"15 years", |
|
|
"10 years", |
|
|
"5 years", |
|
|
"Experience in legal profession:\n(Baseline = No experience)", |
|
|
"2 children", |
|
|
"1 child", |
|
|
"Parental status:\n(Baseline = No children)", |
|
|
"Married", |
|
|
"Marital status:\n(Baseline = Single)", |
|
|
"76 years old", |
|
|
"68 years old", |
|
|
"60 years old", |
|
|
"52 years old", |
|
|
"Age:\n(Baseline = 44 years old)", |
|
|
"Hispanic", |
|
|
"Black", |
|
|
"Asian American", |
|
|
"Race/Ethnicity:\n(Baseline = White)", |
|
|
"Female", |
|
|
"Sex:\n(Baseline = Male)")) |
|
|
) |
|
|
return(pa) |
|
|
} |
|
|
``` |
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan$type1)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r acie_mutate function} |
|
|
acie_mutate <- function(dat1, dat2, dat3, mod){ |
|
|
d1 <- dat1 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Partisanship = "Democrat") |
|
|
d2 <- dat2 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Partisanship = "Independent") |
|
|
d3 <- dat3 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Partisanship = "Republican") |
|
|
dd <- bind_rows(d1, d2, d3) %>% |
|
|
mutate(Partisanship = factor(Partisanship, levels = c("Republican", |
|
|
"Independent", |
|
|
"Democrat"))) |
|
|
return(dd) |
|
|
} |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_partisan1} |
|
|
df_acie_partisan1 <- acie_mutate(summary(acie_partisan$type1)$Partisanship1amce, |
|
|
summary(acie_partisan$type1)$Partisanship2amce, |
|
|
summary(acie_partisan$type1)$Partisanship3amce, |
|
|
mod = acie_partisan$type1) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Partisanship == "Democrat" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r conjoint_plot function} |
|
|
conjoint_plot <- function(data, |
|
|
ylim, xlim, xlab, |
|
|
facet_vari = NULL, |
|
|
text_x, text_y){ |
|
|
pl <- data %>% |
|
|
ggplot(., aes(x = Estimate, y = Level, |
|
|
xmin = lwr, xmax = upr), col = "black") + |
|
|
geom_vline(xintercept = 0, size = .5, |
|
|
colour = "black", linetype = "dotted") + |
|
|
geom_pointrange() + |
|
|
coord_cartesian(ylim = ylim, xlim = xlim, clip = 'off') + |
|
|
labs(x = xlab, y = NULL) + |
|
|
theme(legend.position = "none", |
|
|
axis.text = element_text(size = 11), |
|
|
axis.title = element_text(size = 12), |
|
|
plot.margin = unit(c(1, 1, 0, 1), "lines")) |
|
|
|
|
|
if(is.null(facet_vari)){ |
|
|
pl <- pl + |
|
|
geom_text(x = text_x, y = text_y, hjust = 0, col = "black", |
|
|
size = 4, |
|
|
label = "Judge's attributes", show.legend = FALSE) |
|
|
return(pl) |
|
|
} else { |
|
|
facet_vari <- sym(facet_vari) |
|
|
pl <- pl + |
|
|
facet_wrap(facet_vari, ncol = 3) + |
|
|
geom_text(data = data, |
|
|
x = text_x, y = text_y, hjust = 0, col = "black", |
|
|
size = 4, |
|
|
label = data$judge_lab, show.legend = FALSE) |
|
|
return(pl) |
|
|
} |
|
|
} |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_partisan1, fig.width=10, fig.height=10} |
|
|
pl_acie_partisan1 <- df_acie_partisan1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .2), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
facet_vari = "Partisanship", |
|
|
text_x = -.38, text_y = 26.7) |
|
|
|
|
|
pl_acie_partisan1 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part1} |
|
|
ggsave("output_figure/appendix/Part1.png", pl_acie_partisan1, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r acie_partisan_case} |
|
|
acie_partisan_case <- df_conjoint %>% |
|
|
drop_na(Partisanship) %>% |
|
|
split(list(.$type ,.$Condition)) %>% |
|
|
map( |
|
|
~ amce(selected ~ (Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`) * Partisanship, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines, |
|
|
respondent.varying = c("Partisanship")) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r conjoint_mutate} |
|
|
conjoint_mutate <- function(data, |
|
|
p1 = "", p2 = "", p3 = "", |
|
|
xlim, xlab = ""){ |
|
|
cm <- data %>% |
|
|
mutate( |
|
|
Partisanship = c(rep(p1, nrow(.) / 3), |
|
|
rep(p2, nrow(.) / 3), |
|
|
rep(p3, nrow(.) / 3)), |
|
|
Partisanship = factor(Partisanship, levels = c("Republican", |
|
|
"Independent", |
|
|
"Democrat")), |
|
|
lwr = Estimate - 1.96 * `Std. Err`, |
|
|
upr = Estimate + 1.96 * `Std. Err` |
|
|
) %>% |
|
|
filter(Level %in% c("Female", "Hispanic")) %>% |
|
|
mutate( |
|
|
judge = if_else(Level == "Female", "Female Judge", "Hispanic Judge") |
|
|
) |
|
|
pl <- cm %>% |
|
|
mutate(judge = fct_rev(fct_inorder(judge))) %>% |
|
|
ggplot(aes(x = Estimate, y = Partisanship, shape = judge, color = judge, |
|
|
xmin = lwr, xmax = upr)) + |
|
|
geom_vline(xintercept = 0, size = 1, |
|
|
colour = "gray75", linetype = "solid") + |
|
|
geom_pointrangeh(position = position_dodgev(height = .75), size = .65) + |
|
|
labs(x = xlab, y = NULL) + |
|
|
xlim(xlim) + |
|
|
scale_colour_manual(name = "Judge", |
|
|
values = c(`Female Judge` = "black", |
|
|
`Hispanic Judge` = "grey60"), |
|
|
guide = guide_legend(reverse = TRUE)) + |
|
|
scale_shape_manual(name = "Judge", |
|
|
values = c(`Female Judge` = 15, |
|
|
`Hispanic Judge` = 17), |
|
|
guide = guide_legend(reverse = TRUE)) + |
|
|
theme(legend.position = "bottom", |
|
|
legend.key = element_rect(fill = "white"), |
|
|
axis.text = element_text(size = 11)) |
|
|
|
|
|
return(pl) |
|
|
} |
|
|
``` |
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type1.Conjoint1)$table_values_amce |
|
|
``` |
|
|
|
|
|
```{r pl_part2} |
|
|
pl_part2 <- bind_rows(summary(acie_partisan_case$type1.Conjoint1)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type1.Conjoint1)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type1.Conjoint1)$Partisanship3amce) %>% |
|
|
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican", |
|
|
xlim = c(-.11, .11), xlab = "Change in Pr(Personal values)") |
|
|
pl_part2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save pl_part2} |
|
|
ggsave("output_figure/appendix/Part2.png", pl_part2, width = 6, height = 2.8) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type1.Conjoint2)$table_values_amce |
|
|
``` |
|
|
|
|
|
```{r pl_part3} |
|
|
pl_part3 <- bind_rows(summary(acie_partisan_case$type1.Conjoint2)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type1.Conjoint2)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type1.Conjoint2)$Partisanship3amce) %>% |
|
|
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican", |
|
|
xlim = c(-.2, .2), xlab = "Change in Pr(Personal values)") |
|
|
|
|
|
pl_part3 |
|
|
``` |
|
|
|
|
|
```{r save pl_part3} |
|
|
ggsave("output_figure/appendix/Part3.png", pl_part3, width = 6, height = 2.8) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r acie_pool} |
|
|
acie_pool <- df_conjoint %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_pool1} |
|
|
df_acie_pool1 <- summary(acie_pool$type1)$amce %>% |
|
|
base_level(mod = acie_pool$type1) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_pool1, fig.width=10, fig.height=10} |
|
|
pl_acie_pool1 <- df_acie_pool1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
text_x = -.38, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool1 |
|
|
``` |
|
|
|
|
|
```{r save Part4_a} |
|
|
ggsave("output_figure/appendix/Part4_a.png", pl_acie_pool1, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r df_acie_pool2} |
|
|
df_acie_pool2 <- summary(acie_pool$type2)$amce %>% |
|
|
base_level(mod = acie_pool$type2) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_pool2, fig.width=10, fig.height=10} |
|
|
pl_acie_pool2 <- df_acie_pool2 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .15), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
text_x = -.22, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part4_b} |
|
|
ggsave("output_figure/appendix/Part4_b.png", pl_acie_pool2, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r acie_pool_case} |
|
|
acie_pool_case <- df_conjoint %>% |
|
|
split(list(.$type, .$Condition)) %>% |
|
|
map( |
|
|
~ amce(selected ~ Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r df_acie_pool_case1} |
|
|
df_acie_pool_case1 <- summary(acie_pool_case$type1.Conjoint1)$amce %>% |
|
|
base_level(mod = acie_pool_case$type1.Conjoint1) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r pl_acie_pool_case1, fig.width=10, fig.height=10} |
|
|
pl_acie_pool_case1 <- df_acie_pool_case1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
text_x = -.147, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool_case1 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part5_a} |
|
|
ggsave("output_figure/appendix/Part5_a.png", pl_acie_pool_case1, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r df_acie_pool_case2} |
|
|
df_acie_pool_case2 <- summary(acie_pool_case$type2.Conjoint1)$amce %>% |
|
|
base_level(mod = acie_pool_case$type2.Conjoint1) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_pool_case2, fig.width=10, fig.height=10} |
|
|
pl_acie_pool_case2<- df_acie_pool_case2 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .1), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
text_x = -.21, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool_case2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part5_b} |
|
|
ggsave("output_figure/appendix/Part5_b.png", pl_acie_pool_case2, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r df_acie_pool_case11} |
|
|
df_acie_pool_case11 <- summary(acie_pool_case$type1.Conjoint2)$amce %>% |
|
|
base_level(mod = acie_pool_case$type1.Conjoint2) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r pl_acie_pool_case11, fig.width=10, fig.height=10} |
|
|
pl_acie_pool_case11 <- df_acie_pool_case11 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.1, .1), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
text_x = -.147, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool_case11 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part6_a} |
|
|
ggsave("output_figure/appendix/Part6_a.png", pl_acie_pool_case11, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r df_acie_pool_case22} |
|
|
df_acie_pool_case22 <- summary(acie_pool_case$type2.Conjoint2)$amce %>% |
|
|
base_level(mod = acie_pool_case$type2.Conjoint2) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", "Judge's attributes", NA_character_) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r pl_acie_pool_case22, fig.width=10, fig.height=10} |
|
|
pl_acie_pool_case22 <- df_acie_pool_case22 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.15, .1), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
text_x = -.21, text_y = 26.7) |
|
|
|
|
|
pl_acie_pool_case22 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part6_b} |
|
|
ggsave("output_figure/appendix/Part6_b.png", pl_acie_pool_case22, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type1.Conjoint1)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_partisan_case1} |
|
|
df_acie_partisan_case1 <- acie_mutate( |
|
|
summary(acie_partisan_case$type1.Conjoint1)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type1.Conjoint1)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type1.Conjoint1)$Partisanship3amce, |
|
|
mod = acie_partisan_case$type1.Conjoint1 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Partisanship == "Democrat" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_partisan_case1, fig.width=10, fig.height=10} |
|
|
pl_acie_partisan_case1 <- df_acie_partisan_case1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
facet_vari = "Partisanship", |
|
|
text_x = -.335, text_y = 26.7) |
|
|
|
|
|
pl_acie_partisan_case1 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part7_a} |
|
|
ggsave("output_figure/appendix/Part7_a.png", |
|
|
pl_acie_partisan_case1, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type2.Conjoint1)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_partisan_case2} |
|
|
df_acie_partisan_case2 <- acie_mutate( |
|
|
summary(acie_partisan_case$type2.Conjoint1)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type2.Conjoint1)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type2.Conjoint1)$Partisanship3amce, |
|
|
mod = acie_partisan_case$type2.Conjoint1 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Partisanship == "Democrat" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_partisan_case2, fig.width=10, fig.height=10} |
|
|
pl_acie_partisan_case2 <- df_acie_partisan_case2 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.2, .3), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
facet_vari = "Partisanship", |
|
|
text_x = -.52, text_y = 26.7) |
|
|
|
|
|
pl_acie_partisan_case2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part7_b} |
|
|
ggsave("output_figure/appendix/Part7_b.png", |
|
|
pl_acie_partisan_case2, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type1.Conjoint2)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_partisan_case11} |
|
|
df_acie_partisan_case11 <- acie_mutate( |
|
|
summary(acie_partisan_case$type1.Conjoint2)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type1.Conjoint2)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type1.Conjoint2)$Partisanship3amce, |
|
|
mod = acie_partisan_case$type1.Conjoint2 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Partisanship == "Democrat" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r pl_acie_partisan_case11, fig.width=10, fig.height=10} |
|
|
pl_acie_partisan_case11 <- df_acie_partisan_case11 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
facet_vari = "Partisanship", |
|
|
text_x = -.34, text_y = 26.7) |
|
|
|
|
|
pl_acie_partisan_case11 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part8_a} |
|
|
ggsave("output_figure/appendix/Part8_a.png", |
|
|
pl_acie_partisan_case11, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_partisan_case$type2.Conjoint2)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_partisan_case22} |
|
|
df_acie_partisan_case22 <- acie_mutate( |
|
|
summary(acie_partisan_case$type2.Conjoint2)$Partisanship1amce, |
|
|
summary(acie_partisan_case$type2.Conjoint2)$Partisanship2amce, |
|
|
summary(acie_partisan_case$type2.Conjoint2)$Partisanship3amce, |
|
|
mod = acie_partisan_case$type2.Conjoint2 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Partisanship == "Democrat" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_partisan_case22, fig.width=10, fig.height=10} |
|
|
pl_acie_partisan_case22 <- df_acie_partisan_case22 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.2, .3), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
facet_vari = "Partisanship", |
|
|
text_x = -.52, text_y = 26.7) |
|
|
|
|
|
pl_acie_partisan_case22 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part8_b} |
|
|
ggsave("output_figure/appendix/Part8_b.png", |
|
|
pl_acie_partisan_case22, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r acie_pa} |
|
|
acie_pa <- df_conjoint %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`+ |
|
|
Sex * `Party affiliation` + |
|
|
Age * `Party affiliation` + |
|
|
`Race/Ethnicity` * `Party affiliation` + |
|
|
`Marital status` * `Party affiliation` + |
|
|
`Parental status` * `Party affiliation` + |
|
|
`Experience in legal profession` * `Party affiliation` + |
|
|
`Law school ranking` * `Party affiliation`, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r base_level_pa function} |
|
|
base_level_pa <- function(data, type = ""){ |
|
|
df_base_pa <- summary(acie_pa$type1)$baselines_acie %>% |
|
|
mutate( |
|
|
Attribute = str_remove_all(Attribute, c("Party affiliation" = "", ":" = "")), |
|
|
Level = str_remove_all(Level, c("Democratic Party" = "", ":" = "")), |
|
|
Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")") |
|
|
) |
|
|
pa <- data %>% |
|
|
mutate( |
|
|
Attribute = str_remove_all(Attribute, c("Party affiliation" = "", ":" = "")), |
|
|
Level = str_remove_all(Level, c("Republican Party" = "", ":" = "")), |
|
|
lwr = Estimate - 1.96 * `Std. Err`, |
|
|
upr = Estimate + 1.96 * `Std. Err` |
|
|
) %>% |
|
|
bind_rows(df_base_pa) %>% |
|
|
mutate( |
|
|
Level = as.factor(Level), |
|
|
Level = factor(Level, |
|
|
levels = c("151-200 (Tier 4)", |
|
|
"50-100 (Tier 2)", |
|
|
"Law school ranking:\n(Baseline = Top 10 (Tier 1))", |
|
|
"20 years", |
|
|
"15 years", |
|
|
"10 years", |
|
|
"5 years", |
|
|
"Experience in legal profession:\n(Baseline = No experience)", |
|
|
"2 children", |
|
|
"1 child", |
|
|
"Parental status:\n(Baseline = No children)", |
|
|
"Married", |
|
|
"Marital status:\n(Baseline = Single)", |
|
|
"76 years old", |
|
|
"68 years old", |
|
|
"60 years old", |
|
|
"52 years old", |
|
|
"Age:\n(Baseline = 44 years old)", |
|
|
"Hispanic", |
|
|
"Black", |
|
|
"Asian American", |
|
|
"Race/Ethnicity:\n(Baseline = White)", |
|
|
"Female", |
|
|
"Sex:\n(Baseline = Male)")), |
|
|
type = type |
|
|
) |
|
|
return(pa) |
|
|
} |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_pa} |
|
|
df_acie_pa1 <- summary(acie_pa$type1)$acie %>% |
|
|
base_level_pa(type = "(a) ACIE (personal values influence decisions)") |
|
|
df_acie_pa2 <- summary(acie_pa$type2)$acie %>% |
|
|
base_level_pa(type = "(b) ACIE (judge is biased)") |
|
|
df_acie_pa <- bind_rows(df_acie_pa1, df_acie_pa2) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Party_affiliation = "ACIE\n Party affiliation = Republican Party" |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_pa, fig.width=10, fig.height=10} |
|
|
pl_acie_pa <- df_acie_pa %>% |
|
|
group_nest(type) %>% |
|
|
mutate( |
|
|
gg = map(data, ~ ggplot(., aes(x = Estimate, y = Level, |
|
|
xmin = lwr, xmax = upr), col = "black") + |
|
|
facet_wrap(~ Party_affiliation, ncol = 4) + |
|
|
geom_vline(xintercept = 0, size = .5, |
|
|
colour = "black", linetype = "dotted") + |
|
|
geom_pointrange() + |
|
|
coord_cartesian(ylim = c(1, 24.5), |
|
|
xlim = c(-.1, .1), clip = 'off') + |
|
|
ylab(NULL) + |
|
|
theme(legend.position = "none", |
|
|
axis.text = element_text(size = 11), |
|
|
plot.margin = unit(c(1, 1, 0, 1), "lines"), |
|
|
plot.title = element_text(size = 12, hjust = 0.5))), |
|
|
gg = map2(gg, type, ~ .x + labs(title = .y)) |
|
|
) |
|
|
|
|
|
pl_acie_pa <- wrap_plots(pl_acie_pa$gg) |
|
|
pl_acie_pa[[1]] <- pl_acie_pa[[1]] + |
|
|
geom_text(data = df_acie_partisan_case22, |
|
|
x = -.19, y = 24.7, hjust = 0, col = "black", |
|
|
size = 4, |
|
|
label = df_acie_partisan_case22$judge_lab, |
|
|
show.legend = FALSE) + |
|
|
xlab("Change in Pr(Self value influence judge)") |
|
|
pl_acie_pa[[2]] <- pl_acie_pa[[2]] + |
|
|
xlab("Change in Pr(Biased judge)") + |
|
|
theme(axis.text.y = element_blank()) |
|
|
pl_acie_pa |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part9_ab} |
|
|
ggsave("output_figure/appendix/Part9_ab.png", pl_acie_pa, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```{r acie_context} |
|
|
acie_context <- df_conjoint %>% |
|
|
drop_na(Context2) %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ (Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`) * Context2, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines, |
|
|
respondent.varying = c("Context2")) |
|
|
) |
|
|
``` |
|
|
|
|
|
```{r} |
|
|
summary(acie_context$type1)$table_values_amce |
|
|
``` |
|
|
|
|
|
|
|
|
```{r mutate_context function} |
|
|
mutate_context <- function(dat1, dat2, dat3, mod){ |
|
|
d1 <- dat1 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Context2 = "Interparty") |
|
|
d2 <- dat2 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Context2 = "IntrapartyD") |
|
|
d3 <- dat3 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Context2 = "IntrapartyR") |
|
|
dd <- bind_rows(d1, d2, d3) %>% |
|
|
mutate(Context2 = factor(Context2, levels = c("Interparty", |
|
|
"IntrapartyD", |
|
|
"IntrapartyR"))) |
|
|
return(dd) |
|
|
} |
|
|
``` |
|
|
|
|
|
```{r df_acie_context1} |
|
|
df_acie_context1 <- mutate_context( |
|
|
summary(acie_context$type1)$Context21amce, |
|
|
summary(acie_context$type1)$Context22amce, |
|
|
summary(acie_context$type1)$Context23amce, |
|
|
mod = acie_context$type1 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Context2 == "Interparty" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Context2 = str_c("Context = ", Context2) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_context1, fig.width=10, fig.height=10} |
|
|
pl_acie_context1 <- df_acie_context1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
facet_vari = "Context2", |
|
|
text_x = -.34, text_y = 26.7) |
|
|
|
|
|
pl_acie_context1 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part9_c} |
|
|
ggsave("output_figure/appendix/Part9_c.png", pl_acie_context1, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_context2} |
|
|
df_acie_context2 <- mutate_context( |
|
|
summary(acie_context$type2)$Context21amce, |
|
|
summary(acie_context$type2)$Context22amce, |
|
|
summary(acie_context$type2)$Context23amce, |
|
|
mod = acie_context$type2 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Context2 == "Interparty" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Context2 = str_c("Context = ", Context2) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_context2, fig.width=10, fig.height=10} |
|
|
pl_acie_context2 <- df_acie_context2 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.13, .2), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
facet_vari = "Context2", |
|
|
text_x = -.34, text_y = 26.7) |
|
|
|
|
|
pl_acie_context2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part9_d} |
|
|
ggsave("output_figure/appendix/Part9_d.png", pl_acie_context2, width = 10, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r acie_rsex} |
|
|
acie_rsex <- df_conjoint %>% |
|
|
drop_na(Rsex) %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ (Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`) * Rsex, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines, |
|
|
respondent.varying = c("Rsex")) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_rsex$type1)$table_values_amce |
|
|
``` |
|
|
|
|
|
```{r mutate_rsex function} |
|
|
mutate_resex <- function(dat1, dat2, mod){ |
|
|
d1 <- dat1 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rsex = "Female") |
|
|
d2 <- dat2 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rsex = "Male") |
|
|
dd <- bind_rows(d1, d2) %>% |
|
|
mutate(Rsex = factor(Rsex, levels = c("Female", "Male"))) |
|
|
return(dd) |
|
|
} |
|
|
``` |
|
|
|
|
|
```{r df_acie_rsex} |
|
|
df_acie_rsex1 <- mutate_resex( |
|
|
summary(acie_rsex$type1)$Rsex1amce, |
|
|
summary(acie_rsex$type1)$Rsex2amce, |
|
|
mod = acie_rsex$type1 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Rsex == "Female" & Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Rsex = str_c("Respondent's\n Gender = ", Rsex), |
|
|
type = "(a) ACIE (personal values influence decisions)" |
|
|
) |
|
|
df_acie_rsex2 <- mutate_resex( |
|
|
summary(acie_rsex$type2)$Rsex1amce, |
|
|
summary(acie_rsex$type2)$Rsex2amce, |
|
|
mod = acie_rsex$type2 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Rsex = str_c("Respondent's\n Gender = ", Rsex), |
|
|
type = "(b) ACIE (judge is biased)" |
|
|
) |
|
|
df_acie_rsex <- bind_rows(df_acie_rsex1, df_acie_rsex2) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r pl_acie_rsex, fig.width=14, fig.height=10} |
|
|
pl_acie_rsex <- df_acie_rsex %>% |
|
|
group_nest(type) %>% |
|
|
mutate( |
|
|
gg = map(data, ~ ggplot(., aes(x = Estimate, y = Level, |
|
|
xmin = lwr, xmax = upr), color = "black") + |
|
|
facet_wrap(~ Rsex, ncol = 4) + |
|
|
geom_vline(xintercept = 0, size = .5, |
|
|
colour = "black", linetype = "dotted") + |
|
|
geom_pointrange() + |
|
|
coord_cartesian(ylim = c(1, 26.5), |
|
|
xlim = c(-.15, .1), clip = 'off') + |
|
|
ylab(NULL) + |
|
|
theme(legend.position = "none", |
|
|
axis.text = element_text(size = 11), |
|
|
plot.margin = unit(c(1, 1, 0, 1), "lines"), |
|
|
plot.title = element_text(size = 12, hjust = 0.5))), |
|
|
gg = map2(gg, type, ~ .x + labs(title = .y)) |
|
|
) |
|
|
|
|
|
pl_acie_rsex <- wrap_plots(pl_acie_rsex$gg) |
|
|
pl_acie_rsex[[1]] <- pl_acie_rsex[[1]] + |
|
|
geom_text(data = df_acie_rsex1, |
|
|
x = -.305, y = 26.7, hjust = 0, col = "black", |
|
|
size = 4, |
|
|
label = df_acie_rsex1$judge_lab, |
|
|
show.legend = FALSE) + |
|
|
xlab("Change in Pr(Self value influence judge)") |
|
|
pl_acie_rsex[[2]] <- pl_acie_rsex[[2]] + |
|
|
xlab("Change in Pr(Biased judge)") + |
|
|
theme(axis.text.y = element_blank()) |
|
|
|
|
|
pl_acie_rsex |
|
|
``` |
|
|
|
|
|
```{r save Part10} |
|
|
ggsave("output_figure/appendix/Part10.png", pl_acie_rsex, width = 14, height = 10) |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
```{r acie_rrace} |
|
|
acie_rrace <- df_conjoint %>% |
|
|
drop_na(R_race) %>% |
|
|
split(.$type) %>% |
|
|
map( |
|
|
~ amce(selected ~ (Sex + |
|
|
Age + |
|
|
`Race/Ethnicity` + |
|
|
`Marital status` + |
|
|
`Parental status` + |
|
|
`Experience in legal profession` + |
|
|
`Law school ranking` + |
|
|
`Party affiliation`) * R_race, |
|
|
data = ., |
|
|
cluster = TRUE, |
|
|
respondent.id = "respondentIndex", |
|
|
design = conjoint_design, |
|
|
baselines = baselines, |
|
|
respondent.varying = c("R_race")) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r} |
|
|
summary(acie_rrace$type1)$table_values_amce |
|
|
``` |
|
|
|
|
|
```{r mutate_rrace function} |
|
|
mutate_rrace <- function(dat1, dat2, dat3, dat4,dat5, mod){ |
|
|
d1 <- dat1 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rrace = "Asian") |
|
|
d2 <- dat2 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rrace = "Black") |
|
|
d3 <- dat3 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rrace = "Hispanic") |
|
|
d4 <- dat4 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rrace = "Other") |
|
|
d5 <- dat5 %>% |
|
|
base_level(mod = mod) %>% |
|
|
mutate(Rrace = "White") |
|
|
dd <- bind_rows(d1, d2, d3, d4, d5) %>% |
|
|
mutate(Rsex = factor(Rrace, levels = c("Asian", "Black", "Hispanic", |
|
|
"White", "Other"))) |
|
|
return(dd) |
|
|
} |
|
|
``` |
|
|
|
|
|
|
|
|
```{r df_acie_rrace} |
|
|
df_acie_rrace1 <- mutate_rrace( |
|
|
summary(acie_rrace$type1)$Rrace1amce, |
|
|
summary(acie_rrace$type1)$Rrace2amce, |
|
|
summary(acie_rrace$type1)$Rrace3amce, |
|
|
summary(acie_rrace$type1)$Rrace4amce, |
|
|
summary(acie_rrace$type1)$Rrace5amce, |
|
|
mod = acie_rrace$type1 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Rrace == "Asian" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Rrace = str_c("Respondent's\n Race = ", Rrace) |
|
|
) |
|
|
df_acie_rrace2 <- mutate_rrace( |
|
|
summary(acie_rrace$type2)$Rrace1amce, |
|
|
summary(acie_rrace$type2)$Rrace2amce, |
|
|
summary(acie_rrace$type2)$Rrace3amce, |
|
|
summary(acie_rrace$type2)$Rrace4amce, |
|
|
summary(acie_rrace$type2)$Rrace5amce, |
|
|
mod = acie_rrace$type2 |
|
|
) %>% |
|
|
mutate( |
|
|
judge_lab = if_else(Rrace == "Asian" & |
|
|
Level == "Female", |
|
|
"Judge's attributes", NA_character_), |
|
|
Rrace = str_c("Respondent's\n Race = ", Rrace) |
|
|
) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_rrace1, fig.width=13, fig.height=15} |
|
|
pl_acie_rrace1 <- df_acie_rrace1 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.3, .3), |
|
|
xlab = "Change in Pr(Self value influence judge)", |
|
|
facet_vari = "Rrace", |
|
|
text_x = -.6, text_y = 27) |
|
|
|
|
|
pl_acie_rrace1 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part11_a} |
|
|
ggsave("output_figure/appendix/Part11_a.png", pl_acie_rrace1, width = 13, height = 15) |
|
|
``` |
|
|
|
|
|
|
|
|
```{r pl_acie_rrace2, fig.width=13, fig.height=15} |
|
|
pl_acie_rrace2 <- df_acie_rrace2 %>% |
|
|
conjoint_plot(ylim = c(1, 26.5), xlim = c(-.3, .3), |
|
|
xlab = "Change in Pr(Biased judge)", |
|
|
facet_vari = "Rrace", |
|
|
text_x = -.6, text_y = 27) |
|
|
|
|
|
pl_acie_rrace2 |
|
|
``` |
|
|
|
|
|
|
|
|
```{r save Part11_b} |
|
|
ggsave("output_figure/appendix/Part11_b.png", pl_acie_rrace2, width = 13, height = 15) |
|
|
``` |
|
|
|
|
|
|
|
|
|
