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Analysis.R
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library(tidyverse)
options(scipen = 999, digits = 6)
###
library(xtable)
Results$Cheater_p %>% cut_width(.04, boundary = 0) %>% table()
Results$Cheater_p %>% cut_width(.04, boundary = 0) -> Results$Cheat_lvl
mean(Results$Y_200 - Results$Y)
Results %>% summarise(n_ = mean(n),
sd_n = sd(n),
Y_ = mean(Y),
sdY = sd(Y),
J_ = mean(J),
sdJ = sd(J),
avg_t_ = mean(avg_t),
sd_avg_t = sd(avg_t),
p = mean(Cheater_p),
sd_p = sd(Cheater_p),
ATEYI = mean(Y_200 - Y),
sdATEYI = sd(Y_200 - Y),
ATEYII = mean(Y_Shuff - Y),
sdATEYII = sd(Y_Shuff - Y),
ATEJI = mean(J_200 - J),
sdATEJI = sd(J_200 - J),
ATEJII = mean(J_Shuff - J),
sdATEJII = sd(J_Shuff - J),
) %>% View()
xtable(type= "latex")
Results %>% group_by(Cheat_lvl) %>% summarise(median(Y),
median(Y_200),
median(Y_Shuff),
median(J),
median(J_200),
median(J_Shuff)) %>%
xtable(type= "latex")
###
Results$multipl %>% cut_width(300, boundary = 0) %>% table()
Results$multipl %>% cut_width(300, boundary = 0) -> Results$miu_levels
Results %>% group_by(miu_levels, Cheat_lvl) %>%
summarise( n(),
mean(Y_200 - Y),
mean(Y_Shuff - Y),
mean(J_200 - J),
mean(J_Shuff - J)
) %>%
xtable(type= "latex")
?x
###
mean(Results$Y_200 - Results$Y)
mean(Results$Y_Shuff - Results$Y)
median(Results$J_200 - Results$J)
median(Results$J_Shuff - Results$J)
Results %>% mutate(Y1 = Y_200 - Y,
Y2 = Y_Shuff - Y,
J1 = J_200 - J,
J2 = J_Shuff - J) %>%
group_by(Cheat_lvl) %>% summarise(mean(Y1),
mean(Y2),
median(J1),
median(J2)) %>%
xtable(type= "latex", digits =3)
###
ggplot(Results, aes(x = Y_200 - Y,
y = Cheater_p)) +
geom_bin2d(bins = 35) +
scale_fill_binned(type = "viridis",
breaks = c(50,100,250,500,750),
show.limit = T) +
xlim(c(-.2,.2))+
xlab("Y_200 - Y")+
ylab("Cheating") +
ggtitle("Impact on efficacy of small lotteries")+
theme_classic() -> p1
ggplot(Results, aes(x = (J_200 - J) / 100,
y = Cheater_p)) +
geom_bin2d(bins = 50)+
scale_fill_binned(type = "viridis",
breaks = c(50,100,250,500,750,1000),
show.limit = T) +
xlim(c(-.25,.75))+
xlab("J_200 - J")+
ylab("Cheating") +
ggtitle("Impact on efficacy of small lotteries")+
theme_classic() -> p2
ggplot(Results, aes(x = Y_Shuff - Y,
y = Cheater_p)) +
geom_bin2d(bins = 28)+
scale_fill_binned(type = "viridis",
breaks = c(50,100,250,500,750),
show.limit = T) +
xlim(c(-.2,.2))+
xlab("Y_% - Y")+
ylab("Cheating") +
ggtitle("Impact on fairness of big lotteries")+
theme_classic() -> p3
ggplot(Results, aes(x = (J_Shuff - J) / 100,
y = Cheater_p)) +
geom_bin2d(bins = 33)+
scale_fill_binned(type = "viridis",
breaks = c(50,100,250,500,750,1000),
show.limit = T) +
xlab("J_% - J")+
ylab("Cheating") +
xlim(c(-.25,.75))+
ggtitle("Impact on Justice of big lotteries")+
theme_classic() -> p4
###
###
library(broom)
options(scipen = 999, digits = 1)
lm(scale(Y_200 - Y) ~
scale(Cheater_p) +
scale(multipl) +
scale(q), data = Results) %>% tidy() %>%
mutate_if(is.numeric, round, 5)
lm(scale(Y_Shuff - Y) ~
scale(Cheater_p) +
scale(multipl) +
scale(q), data = Results) %>% tidy() %>%
mutate_if(is.numeric, round, 5)
lm(scale(J_200 - J) ~
scale(Cheater_p) +
scale(multipl) +
scale(q), data = Results) %>% tidy() %>%
mutate_if(is.numeric, round, 5)
lm(scale(J_Shuff - J) ~
scale(Cheater_p) +
scale(multipl) +
scale(q), data = Results) %>% tidy() %>%
mutate_if(is.numeric, round, 5)