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#Figure 5
#Coethnic voting preferences in candidate conjoint experiment,
#comparing high- to low-exposure respondents in network dataset.
#install packages
# install.packages('plyr')
# install.packages('dplyr')
# install.packages('tidyr')
# install.packages('ggplot2')
# install.packages('multiwayvcov')
# install.packages('lmtest')
# install.packages('stargazer')
rm(list=ls())
#setwd() #set working directory
dir.create(paste0(getwd(), '/Output/'))
dir.create(paste0(getwd(), '/Output/Figure_5/'))
path0 = paste0(getwd(), '/Output/Figure_5/', Sys.Date(),'/') #Directory for output files
dir.create(path0)
library(plyr);library(dplyr, warn.conflicts = FALSE)
library(tidyr);library(ggplot2)
suppressMessages(library(multiwayvcov, warn.conflicts = F))
suppressMessages(library(lmtest, warn.conflicts = F))
suppressMessages(library(stargazer))
s = function(x){summary(factor(x))}
A = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
########################################################################################################################
#Balance tables
A$HiSeg = A$Nearest10_SameRel == 10
A$HiSeg_DeMed = A$DeMedNearest10_SameRel >= 0
A$LoSeg_DeMed = A$DeMedNearest10_SameRel < 0
A$LowCaste = A$C.C8_Caste == 'SC/ST'
A$Muslim = A$C.C6 == 'Muslim'
A$Male = A$C.C5_Gender == 'M'
A$Migrant = A$C.C14_Live.in.Jaipur. == 0
A$Jaipur = A$City == 'Jaipur'
A$Patna = A$City == 'Patna'
A$C.C4_Age = as.numeric(as.character(A$C.C4_Age))
A$Income1 = mapvalues(A$F.F1_Monthly.Income, from = c(-888, 0, 888, 999), to = c(NA, NA, NA, NA)) / 1000
A$Income2 = A$Income1 > 10000
#Non-de-medianed
bal.vars = c('Income1','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur')
bal.table = data.frame('Segregated' = apply(A[A$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
'Integrated' = apply(A[!A$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
'p' = apply(A[,bal.vars],2,function(x){t.test(x[A$HiSeg],
x[!A$HiSeg])[['p.value']]}) ) %>%
round(2)
bal.table = rbind(bal.table, data.frame('Segregated' = sum(A$HiSeg == 1, na.rm = T),
'Integrated' = sum(A$HiSeg == 0, na.rm = T), 'p' = ''))
row.names(bal.table) = c('Income (k INR/mo.)','Low Caste','Muslim','Male','Age','Migrant','Jaipur','n')
bal.table
out = stargazer(bal.table, summary = F, digits = 2,
title = 'Balance Table, Segregated vs. Integrated',
label = 'table:Nearest10Religion_Balance')
writeLines(out,con = paste0(path0,'Network_Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
########################################################################################################################
#Begin conjoint analysis
Q = A
#Rename variables
Q = Q %>% dplyr::rename(Q1A1 = I.Neta_Random_A1, #first question, candidate 1, characteristic A
Q1A2 = I.Neta_Random_A2, #first question, candidate 2, characteristic A
Q1B1 = I.Neta_Random_B1, #first question, candidate 1, characteristic B
Q1B2 = I.Neta_Random_B2, #first question, candidate 2, characteristic B
Q2A1 = I.Neta_Random_A3, #second question, candidate 1, characteristic A
Q2A2 = I.Neta_Random_A4, #second question, candidate 2, characteristic A
Q2B1 = I.Neta_Random_B3, #second question, candidate 1, characteristic B
Q2B2 = I.Neta_Random_B4, #second question, candidate 2, characteristic B
Q3A1 = I.Neta_Random_A5, #third question, candidate 1, characteristic A
Q3A2 = I.Neta_Random_A6, #third question, candidate 2, characteristic A
Q3B1 = I.Neta_Random_B5, #third question, candidate 1, characteristic B
Q3B2 = I.Neta_Random_B6, #third question, candidate 2, characteristic B
Q1 = I.Neta_Question_1, #first question, choose candidate 1 or 2
Q2 = I.Neta_Question_2, #first question, choose candidate 1 or 2
Q3 = I.Neta_Question_3 #first question, choose candidate 1 or 2
)
#rearrange so one row is one conjoint observation. 3x as many rows as A
#new variables: A1, B1 are two traits for candidate 1; similar for 2; and y is responent's choice between candidates
B = Q %>% unite('Q1', matches('Q1')) %>% unite('Q2', matches('Q2')) %>% unite('Q3', matches('Q3')) %>%
gather(Question, b, starts_with('Q')) %>% arrange(X) %>% separate( 'b', c('A1','A2','B1','B2','y') )
B = B %>% filter(! (A1 == A2 & B1 == B2)) #drop observations where candidates have same profile
#make new data frame wheer each row is one PROFILE, ie each question becomes two rows (one for each candidate)
#New variables: A1, A2 are combined as A: trait A for either candidate
C = B %>% unite('AB1',c(A1,B1)) %>% unite('AB2',c(A2,B2)) %>% gather(Neighbor, AB, c(AB1, AB2)) %>%
arrange(X) %>% separate('AB', c('A','B')) %>%
mutate(Neighbor = as.numeric(mapvalues(Neighbor, from = c('AB1', 'AB2'), to = c(1,2))))
#function to make dummies for trait levels
ModFn = function(x,f){
data.frame(x, model.matrix(as.formula(f), data=x)) }
C = C %>% ModFn('~ A - 1') %>% ModFn('~ B - 1') #function to make dummies for trait levels
C = C %>% mutate(Y = y == Neighbor) #1 when that candidate is picked
C = C %>% dplyr::rename(A_0 = A0, #reanme variables to be consistent with earlier version of code
A_1 = A1,
A_2 = A2,
A_3 = A3,
B_0 = B0,
B_1 = B1,
B_2 = B2,
B_3 = B3,
B_4 = B4)
B = C; rm(C) #rename to be consistent with earlier version
###########################################################################################
###########################################################################################
#SUBGROUP ANALYSIS: do analysis and make plots
#Subgroups: Nearest10_SameRel; Caste
form1 = as.formula(paste0('Y ~ ',
paste(strsplit('A_0 A_1 A_3 B_0 B_1 B_2 B_3 B_4',
split = ' ')[[1]], collapse = ' + ' )))
#function to make data frame from regression results
DF_C = function(l_m, id){
l_m_pl = data.frame(Parameter = rownames(l_m[-1,])) %>% #-1 drops intercept
mutate(Coef = l_m[-1,1]) %>%
mutate(Lo = Coef - 1.96 * l_m[-1,2]) %>%
mutate(Hi = Coef + 1.96 * l_m[-1,2]) %>%
mutate(ID = id) %>%
rbind(data.frame(Parameter = c('A_2','B_4'), #6-22-18
Coef = c(0,0), Lo = c(0,0), Hi = c(0,0), ID = c(id, id)) ) %>%
mutate(Parameter = as.character(Parameter)) %>%
arrange(Parameter)
}
#Make functions to extract p and z as a function of k
calc_p_relig = function(k, Dat){
var <- paste0('Nearest',k,'_SameRel')
if(median(Dat[,var],na.rm=T)==k){
var_break <- k-1
}else{var_break = floor(median(Dat[,var],na.rm=T))}
dat_lo = Dat[ which(Dat[,var] <= var_break ) ,]
dat_hi = Dat[ which(Dat[,var] > var_break ) ,]
if(nrow(dat_lo) == 0 | nrow(dat_hi) == 0){p_a2 = NA
}else{
lm_lo = lm(form1, data = dat_lo )
lm_hi = lm(form1, data = dat_hi )
lm_clus_lo = coeftest(lm_lo, cluster.vcov(lm_lo, dat_lo[,c('X','A.A7')]))
lm_clus_hi = coeftest(lm_hi, cluster.vcov(lm_hi, dat_hi[,c('X','A.A7')]))
z_a2 = (lm_clus_lo[3,1] - lm_clus_hi[3,1]) / sqrt(lm_clus_lo[3,2]^2 + lm_clus_hi[3,2]^2)
p_a2 = 2 * pnorm( abs(z_a2), mean = 0, sd = 1, lower.tail = F)
coef_lo = lm_clus_lo[3,1]; coef_hi = lm_clus_hi[3,1]
sd_lo = lm_clus_lo[3,2]; sd_hi = lm_clus_hi[3,2]
return(data.frame(coef_lo, coef_hi, sd_lo, sd_hi, p_a2)) }
}
#calculate results for all k
relig_results = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B)) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
#Create table
relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>%
rename(Coef_LowSeg = coef_lo, SD_LowSeg = sd_lo, Coef_HiSeg = coef_hi, SD_HiSeg = sd_hi, p = p_a2) %>%
round(3)
out = stargazer(relig_results_tab, summary = F, rownames = F,
title = 'Results for co-ethnicity attribute in candidate experiment compared between
high- and low-segregation subsamples, based on religious exposure.',
label = 'table:ReligResults_Network')
writeLines(out,con = paste0(path0,'ReligResults_Network.tex'));rm(out, relig_results_tab)
#Create data frame for coefficient plots
rrc =
relig_results[seq(from = 1, to = 30, by = 1),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Create plot
ggplot(data = rrc, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_all.png'), height = 150, width = 150, units = 'mm')
################################################################################################################################
#Income: median is 10k
relig_results_rich = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Income1 >= 10),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_rich =
relig_results_rich[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#FIGURE A12
ggplot(data = rrc_rich, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) + #
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (High Income)') +
guides(shape = guide_legend(order = 1), #
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_rich.png'), height = 150, width = 150, units = 'mm')
#Patna
relig_results_patna = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$City == 'Patna'),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_patna =
relig_results_patna[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#FIGURE A13
ggplot(data = rrc_patna, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Patna)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_patna.png'), height = 150, width = 150, units = 'mm')
#Female
relig_results_female = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Male == FALSE),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_female =
relig_results_female[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Figure A14
ggplot(data = rrc_female, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Female)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_female.png'), height = 150, width = 150, units = 'mm')
#Migrant
relig_results_migrant = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$Migrant == TRUE),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_migrant =
relig_results_migrant[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Figure A15
ggplot(data = rrc_migrant, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Migrant)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_migrant.png'), height = 150, width = 150, units = 'mm')
#High Caste
relig_results_hicaste = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$LowCaste == FALSE),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_hicaste =
relig_results_hicaste[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Figure A16
ggplot(data = rrc_hicaste, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Hi Caste)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_hicaste.png'), height = 150, width = 150, units = 'mm')
#Muslim
relig_results_mus = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6 == 'Muslim'),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_mus =
relig_results_mus[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Figure A17
ggplot(data = rrc_mus, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Muslim)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_muslim.png'), height = 150, width = 150, units = 'mm')
#Hindu
relig_results_hin = sapply(1:30, function(x) calc_p_relig(k=x, Dat = B[which(B$C.C6 == 'Hindu'),])) %>% t() %>% data.frame() %>%
mutate(k = 1:30,
dif = as.numeric(coef_lo) - as.numeric(coef_hi),
dif_sd = sqrt(as.numeric(sd_lo)^2 + as.numeric(sd_hi)^2),
dif_lobd = dif - 1.96*dif_sd,
dif_hibd = dif + 1.96*dif_sd
)
rrc_hin =
relig_results_hin[seq(from = 2, to = 30, by = 3),] %>%
rename(Low = coef_lo, High = coef_hi) %>%
select(-starts_with('dif')) %>%
gather(Seg, Coef, c(Low, High)) %>% mutate(Coef = as.numeric(Coef),
sd_lo = as.numeric(sd_lo),
sd_hi = as.numeric(sd_hi),
p_a2 = as.numeric(p_a2),
sd = sd_lo*(Seg == 'Low') + sd_hi*(Seg == 'High'),
lo = Coef - 1.96*sd,
hi = Coef + 1.96*sd,
sig = factor(p_a2 < 0.05))
#Figure A18
ggplot(data = rrc_hin, aes(x = k, y = Coef)) +
geom_pointrange(aes(ymin = lo, ymax = hi, x = k, shape = Seg, alpha = sig),
position = position_dodge(width = 0.9)) +
scale_alpha_manual('p < 0.05', c(TRUE, FALSE), values=c(1, 0.5), labels = c('Yes','No')) +
scale_shape_manual('Exposure', values = c(16, 17), labels = c('Low','High')) +
labs(shape = 'Exposure') +
theme_minimal() + ylab('Coethnicity Coefficients') +
theme(text = element_text(size = 16),
plot.title = element_text(hjust = 0.5)) +
ggtitle('k-Nearest Own Religion (Hindu)') +
guides(shape = guide_legend(order = 1),
alpha = guide_legend(order = 0))
ggsave(filename = paste0(path0, 'k-z_coefficients_NETWORK_hindu.png'), height = 150, width = 150, units = 'mm')
######################################################################################################
#Main network results in table
#Table A7
relig_results_tab = relig_results %>% apply(2, function(x) as.numeric(x)) %>% data.frame() %>%
select(k, coef_lo, sd_lo, coef_hi, sd_hi, p_a2) %>%
rename(Coef_LowSeg = coef_lo, SD_LowSeg = sd_lo, Coef_HiSeg = coef_hi, SD_HiSeg = sd_hi, p = p_a2) %>%
round(3)
out = stargazer(relig_results_tab, summary = F, rownames = F,
title = 'Results for co-ethnicity attribute in candidate experiment compared between
high- and low-segregation subsamples, based on religious segregation.',
label = 'table:ReligResults_Network')
writeLines(out,con = paste0(path0,'ReligResults_Network.tex'));rm(out, path0, relig_results_tab)
####################################################################################################################### |