This R Markdown document contains the code for performing simulation studies related to the present method for setting acceptance criteria.
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(latex2exp)
load("sim_results.RData")
ls_m_breaks <- c(4, 6, 8, 10)
ls_m_values <- c("solid", "longdash", "dotdash", "dotted")
g <- sim_equiv %>%
mutate(m = as.factor(m)) %>%
group_by(n, m, method) %>%
ggplot(aes(x = n, y = `Rejection Rate`,
color = m, linetype = m)) +
geom_line() +
facet_grid(. ~ method) +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 0.15)) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
ggsave(filename = "figure01.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
g
g <- sim_equiv %>%
mutate(m = as.factor(m)) %>%
filter(method == "Two-Sample") %>%
ggplot(aes(x = n, y = `Rejection Rate`,
color = m, linetype = m)) +
geom_line() +
ylim(0, 0.1) +
theme_bw() +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
g
ls_method_breaks <- c("Two-Sample", "Vangel", "MSD")
ls_method_values <- c("solid", "dashed", "dotted")
g <- sim_power_mean %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 1.5, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure05.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
ls_n_breaks <- c(18, 30, 100)
ls_n_values <- c("solid", "dashed", "dotted")
g <- sim_power_mean %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure02.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_mean %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 1.71
## 2 18 4 1.58
## 3 24 4 1.51
## 4 30 4 1.48
## 5 36 4 1.46
## 6 50 4 1.43
## 7 100 4 1.39
## 8 12 6 1.49
## 9 18 6 1.37
## 10 24 6 1.30
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_mean %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(m = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = m, linetype = m)) +
geom_line() +
ylab(TeX("$\\delta$")) +
xlab("n") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
ggsave(filename = "figure16.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
g
g <- sim_power_sd %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 3.5, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure06.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g <- sim_power_sd %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure03.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_sd %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 NA
## 2 18 4 NA
## 3 24 4 NA
## 4 30 4 NA
## 5 36 4 NA
## 6 50 4 NA
## 7 100 4 NA
## 8 12 6 4.60
## 9 18 6 4.30
## 10 24 6 4.15
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
sim_power_sd %>%
filter(method == "MSD") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 3.45
## 2 18 4 3.22
## 3 24 4 3.09
## 4 30 4 3.01
## 5 36 4 2.98
## 6 50 4 2.93
## 7 100 4 2.88
## 8 12 6 2.89
## 9 18 6 2.65
## 10 24 6 2.51
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_sd %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(m = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = m, linetype = m)) +
geom_line() +
ylab(TeX("$\\delta$")) +
xlab("n") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
ggsave(filename = "figure17.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning: Removed 7 row(s) containing missing values (geom_path).
g
## Warning: Removed 7 row(s) containing missing values (geom_path).
g <- sim_power_min_indiv %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 3, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure07.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g <- sim_power_min_indiv %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure04.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_min_indiv %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 3.86
## 2 18 4 3.61
## 3 24 4 3.47
## 4 30 4 3.41
## 5 36 4 3.38
## 6 50 4 3.32
## 7 100 4 3.25
## 8 12 6 NA
## 9 18 6 3.90
## 10 24 6 3.78
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_min_indiv %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(m = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = m, linetype = m)) +
geom_line() +
ylab(TeX("$\\delta$")) +
xlab("n") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
ggsave(filename = "figure18.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning: Removed 4 row(s) containing missing values (geom_path).
g
## Warning: Removed 4 row(s) containing missing values (geom_path).
g <- sim_power_mixture %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 2.5, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure15.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g <- sim_power_mixture %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure14.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_mixture %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 3.92
## 2 18 4 3.65
## 3 24 4 3.50
## 4 30 4 3.44
## 5 36 4 3.40
## 6 50 4 3.34
## 7 100 4 3.26
## 8 12 6 3.34
## 9 18 6 3.04
## 10 24 6 2.91
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_mixture %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(m = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = m, linetype = m)) +
geom_line() +
ylab(TeX("$\\delta$")) +
xlab("n") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
ggsave(filename = "figure19.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
g
g <- sim_power_lognormal %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 1.5, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure09.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g <- sim_power_lognormal %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure08.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_lognormal %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 1.53
## 2 18 4 1.35
## 3 24 4 1.21
## 4 30 4 1.13
## 5 36 4 1.05
## 6 50 4 0.905
## 7 100 4 0.571
## 8 12 6 1.40
## 9 18 6 1.22
## 10 24 6 1.13
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_lognormal %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(`$m$` = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = `$m$`, linetype = `$m$`)) +
geom_line() +
ylab("$\\delta$") +
xlab("$n$") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
g
g <- sim_power_weibull %>%
filter(m %in% c(6)) %>%
filter(n %in% c(18)) %>%
rename(Method = method) %>%
mutate(Method = fct_relevel(Method, "Two-Sample", "Vangel", "MSD")) %>%
arrange(m) %>%
arrange(n) %>%
mutate(n = as_factor(paste0("n = ", n)),
m = as_factor(paste0("m = ", m))) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = Method, linetype = Method)) +
geom_line() +
geom_hline(yintercept = 0.05, color = "black") +
annotate("text", label = TeX("Nominal $\\alpha = 0.05$"),
x = 1.5, y = 0.08, color = "black",
vjust = "center", size = 3) +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_grid(m ~ n) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_method_breaks,
values = ls_method_values
)
ggsave(filename = "figure11.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 6,
height = 3,
units = "in"
)
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g
## Warning in is.na(x): is.na() applied to non-(list or vector) of type
## 'expression'
g <- sim_power_weibull %>%
filter(method == "Two-Sample") %>%
filter(n %in% ls_n_breaks) %>%
arrange(m) %>%
mutate(m = as_factor(paste0("m = ", m))) %>%
arrange(n) %>%
mutate(n = as_factor(n)) %>%
ggplot(aes(x = delta, y = `Rejection Rate`,
color = n, linetype = n)) +
geom_line() +
xlab(TeX("$\\delta$")) +
theme_bw() +
facet_wrap(vars(m)) +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_y_continuous(expand = expansion(0, 0), limits = c(0, 1)) +
scale_linetype_manual(
breaks = ls_n_breaks,
values = ls_n_values
)
ggsave(filename = "figure10.eps",
plot = g,
device = "eps",
dpi = 1200,
width = 5.5,
height = 3,
units = "in"
)
g
sim_power_weibull %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated))
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## # A tibble: 28 × 3
## # Groups: n, m [28]
## n m `Delta for beta=0.2`
## <dbl> <dbl> <dbl>
## 1 12 4 1.60
## 2 18 4 1.36
## 3 24 4 1.17
## 4 30 4 0.994
## 5 36 4 0.827
## 6 50 4 0.499
## 7 100 4 NA
## 8 12 6 1.46
## 9 18 6 1.25
## 10 24 6 1.13
## # … with 18 more rows
## # ℹ Use `print(n = ...)` to see more rows
g <- sim_power_weibull %>%
filter(method == "Two-Sample") %>%
group_by(n, m) %>%
nest() %>%
mutate(interpolated = map(data, ~approx(.$`Rejection Rate`, .$delta, 0.8))) %>%
mutate(`Delta for beta=0.2` = unlist(map(interpolated, ~.[[2]]))) %>%
select(-c(data, interpolated)) %>%
mutate(`$m$` = as.factor(m)) %>%
ggplot(aes(x = n, y = `Delta for beta=0.2`, color = `$m$`, linetype = `$m$`)) +
geom_line() +
ylab("$\\delta$") +
xlab("$n$") +
theme_bw() +
theme(axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0))) +
scale_linetype_manual(
breaks = ls_m_breaks,
values = ls_m_values
)
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
g
## Warning: Removed 1 row(s) containing missing values (geom_path).
sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] latex2exp_0.9.4 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
## [5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.8
## [9] ggplot2_3.3.6 tidyverse_1.3.2 rmarkdown_2.14
##
## loaded via a namespace (and not attached):
## [1] lubridate_1.8.0 assertthat_0.2.1 digest_0.6.29
## [4] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
## [7] backports_1.4.1 reprex_2.0.1 evaluate_0.15
## [10] highr_0.9 httr_1.4.3 pillar_1.8.0
## [13] rlang_1.0.4 googlesheets4_1.0.0 readxl_1.4.0
## [16] jquerylib_0.1.4 labeling_0.4.2 textshaping_0.3.6
## [19] googledrive_2.0.0 munsell_0.5.0 broom_1.0.0
## [22] compiler_4.2.2 modelr_0.1.8 xfun_0.31
## [25] pkgconfig_2.0.3 systemfonts_1.0.4 htmltools_0.5.3
## [28] tidyselect_1.1.2 fansi_1.0.3 crayon_1.5.1
## [31] tzdb_0.3.0 dbplyr_2.2.1 withr_2.5.0
## [34] grid_4.2.2 jsonlite_1.8.0 gtable_0.3.0
## [37] lifecycle_1.0.1 DBI_1.1.3 magrittr_2.0.3
## [40] scales_1.2.0 cli_3.3.0 stringi_1.7.8
## [43] cachem_1.0.6 farver_2.1.1 fs_1.5.2
## [46] xml2_1.3.3 bslib_0.4.0 ellipsis_0.3.2
## [49] ragg_1.2.2 generics_0.1.3 vctrs_0.4.1
## [52] tools_4.2.2 glue_1.6.2 hms_1.1.1
## [55] fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
## [58] gargle_1.2.0 rvest_1.0.2 knitr_1.39
## [61] haven_2.5.0 sass_0.4.2