Study2 (article)
Yuya Endo
2020-09-18
## My Session info ##
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_1.3.0df_type1 <- read_csv("data/study2_data_type1.csv")
df_type2 <- read_csv("data/study2_data_type2.csv") 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")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")Figure 2
acie_partisan <- df_conjoint %>%
drop_na(Partisanship) %>%
filter(type == "type2") %>%
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"))base_level <- function(data, mod){
df_base_pa <- summary(mod)$baselines_amce %>%
mutate(
Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")")
)
pa <- data %>%
mutate(
lwr = Estimate - 1.96 * `Std. Err`,
upr = Estimate + 1.96 * `Std. Err`
) %>%
bind_rows(df_base_pa) %>%
mutate(
Level = as.factor(Level),
Level = factor(Level,
levels = c("Republican Party",
"Party affiliation:\n(Baseline = Democratic Party)",
"151-200 (Tier 4)",
"50-100 (Tier 2)",
"Law school ranking:\n(Baseline = Top 10 (Tier 1))",
"20 years",
"15 years",
"10 years",
"5 years",
"Experience in legal profession:\n(Baseline = No experience)",
"2 children",
"1 child",
"Parental status:\n(Baseline = No children)",
"Married",
"Marital status:\n(Baseline = Single)",
"76 years old",
"68 years old",
"60 years old",
"52 years old",
"Age:\n(Baseline = 44 years old)",
"Hispanic",
"Black",
"Asian American",
"Race/Ethnicity:\n(Baseline = White)",
"Female",
"Sex:\n(Baseline = Male)"))
)
return(pa)
} Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" acie_mutate <- function(dat1, dat2, dat3, mod){
d1 <- dat1 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Democrat")
d2 <- dat2 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Independent")
d3 <- dat3 %>%
base_level(mod = mod) %>%
mutate(Partisanship = "Republican")
dd <- bind_rows(d1, d2, d3) %>%
mutate(Partisanship = factor(Partisanship, levels = c("Republican",
"Independent",
"Democrat")))
return(dd)
}df_acie_partisan <- acie_mutate(summary(acie_partisan)$Partisanship1amce,
summary(acie_partisan)$Partisanship2amce,
summary(acie_partisan)$Partisanship3amce,
mod = acie_partisan) %>%
mutate(
judge_lab = if_else(Partisanship == "Democrat" &
Level == "Female",
"Judge's attributes", NA_character_),
Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship)
)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$judge_lab, show.legend = FALSE)
return(pl)
}
}pl_acie_partisan <- df_acie_partisan %>%
ggplot(., aes(x = Estimate, y = Level,
xmin = lwr, xmax = upr), col = "black") +
geom_vline(xintercept = 0, size = .5,
colour = "black", linetype = "dotted") +
geom_pointrange() +
facet_wrap(~ Partisanship, ncol = 3) +
geom_text(data = df_acie_partisan,
x = -.49, y = 26.7, hjust = 0, col = "black",
size = 4,
label = df_acie_partisan$judge_lab, show.legend = FALSE) +
coord_cartesian(ylim = c(1, 26.5), xlim = c(-.2, .23), clip = 'off') +
labs(x = "Change in Pr(Biased judge)", 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"))
pl_acie_partisan
Figure 3
acie_partisan_case <- df_conjoint %>%
drop_na(Partisanship) %>%
filter(type == "type2") %>%
split(.$Condition) %>%
map(~ amce(selected ~ (Sex +
Age +
`Race/Ethnicity` +
`Marital status` +
`Parental status` +
`Experience in legal profession` +
`Law school ranking` +
`Party affiliation`) * Partisanship,
data = .,
cluster = TRUE,
respondent.id = "respondentIndex",
design = conjoint_design,
baselines = baselines,
respondent.varying = c("Partisanship"))
)conjoint_mutate <- function(data,
p1 = "", p2 = "", p3 = "",
xlim, xlab = ""){
cm <- data %>%
mutate(
Partisanship = c(rep(p1, nrow(.) / 3),
rep(p2, nrow(.) / 3),
rep(p3, nrow(.) / 3)),
Partisanship = factor(Partisanship, levels = c("Republican",
"Independent",
"Democrat")),
lwr = Estimate - 1.96 * `Std. Err`,
upr = Estimate + 1.96 * `Std. Err`
) %>%
filter(Level %in% c("Female", "Hispanic")) %>%
mutate(
judge = if_else(Level == "Female", "Female Judge", "Hispanic Judge")
)
pl <- cm %>%
mutate(judge = fct_rev(fct_inorder(judge))) %>%
ggplot(aes(x = Estimate, y = Partisanship, shape = judge, color = judge,
xmin = lwr, xmax = upr)) +
geom_vline(xintercept = 0, size = 1,
colour = "gray75", linetype = "solid") +
geom_pointrangeh(position = position_dodgev(height = .75), size = .65) +
labs(x = xlab, y = NULL) +
xlim(xlim) +
scale_colour_manual(name = "Judge",
values = c(`Female Judge` = "black",
`Hispanic Judge` = "grey60"),
guide = guide_legend(reverse = TRUE)) +
scale_shape_manual(name = "Judge",
values = c(`Female Judge` = 15,
`Hispanic Judge` = 17),
guide = guide_legend(reverse = TRUE)) +
theme(legend.position = "bottom",
legend.key = element_rect(fill = "white"),
axis.text = element_text(size = 11))
return(pl)
} Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" pl_abortion <- bind_rows(summary(acie_partisan_case$Conjoint1)$Partisanship1amce,
summary(acie_partisan_case$Conjoint1)$Partisanship2amce,
summary(acie_partisan_case$Conjoint1)$Partisanship3amce) %>%
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican",
xlim = c(-.11, .11), xlab = "Change in Pr(Biased judge)")
pl_abortion
Part 3: Separated Conjoint Results for Immigration Case (Figure 4)
Table.Name Level.Name Level.Value
[1,] "Partisanship1amce" "Partisanship" "Democrat"
[2,] "Partisanship2amce" "Partisanship" "Independent"
[3,] "Partisanship3amce" "Partisanship" "Republican" pl_immigration <- bind_rows(summary(acie_partisan_case$Conjoint2)$Partisanship1amce,
summary(acie_partisan_case$Conjoint2)$Partisanship2amce,
summary(acie_partisan_case$Conjoint2)$Partisanship3amce) %>%
conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican",
xlim = c(-.23, .23), xlab = "Change in Pr(Biased judge)")
pl_immigration