examples/one_constant_cross-correlated_block/low-dim/viz.R
In agisga/FDRcorrectedSCCA: FDR-Corrected Sparse Canonical Correlation Analysis
library(tidyverse)
load_csv_results <- function(subdir) {
results_dir <- "./results/"
if (!grepl(".+/$", subdir)) { subdir <- paste0(subdir, "/") }
subdir_full_path <- paste0(results_dir, subdir)
csv_results <- list.files(subdir_full_path)
# keep csv files only
csv_results <- csv_results[grepl("^.+\\.csv$", csv_results)]
csv_results <- paste0(subdir_full_path, csv_results)
results_df <- NULL
for (filename in csv_results) {
tmp_df <- read_csv(filename, guess_max = 1e6)
if ( !("iter" %in% colnames(tmp_df)) ) {
warning(paste("'iter' not recorded for file", filename))
tmp_df$iter <- 1:nrow(tmp_df)
}
# (setting guess_max to avoid readr from guessing that a column is integer when it is double)
if (is.null(results_df)) {
results_df <- tmp_df
} else {
results_df <- bind_rows(results_df, tmp_df)
}
}
return(results_df)
}
FDRcorrectedSCCA_df <- load_csv_results("constant_low_dimensional_FDRcorrectedSCCA")
SCCA_5foldCV_df <- load_csv_results("constant_low_dimensional_5foldCV")
SCCA_PMA_df <- load_csv_results("constant_low_dimensional_PMA")
ordinary_SCCA_df <- load_csv_results("constant_low_dimensional_ordinary_SCCA")
dim(FDRcorrectedSCCA_df)
# [1] 24000 13
dim(SCCA_5foldCV_df)
# [1] 8000 14
dim(SCCA_PMA_df)
# [1] 8000 14
dim(ordinary_SCCA_df)
# [1] 16000 14
colnames(FDRcorrectedSCCA_df)
colnames(SCCA_5foldCV_df)
colnames(SCCA_PMA_df)
colnames(ordinary_SCCA_df)
FDRcorrectedSCCA_df <- mutate(FDRcorrectedSCCA_df, method = "FDR-corrected SCCA") %>%
mutate(method = paste0(method, " (q = ", target_FDR, ")"))
SCCA_5foldCV_df <- mutate(SCCA_5foldCV_df, method = "L1 SCCA: 5-fold CV")
SCCA_PMA_df <- mutate(SCCA_PMA_df, method = "L1 SCCA: permutation-based")
ordinary_SCCA_df <- mutate(ordinary_SCCA_df, method = "L1 SCCA: fixed penalty") %>%
mutate(method = paste0(method, " (", lambda_X, ")"))
simulation_results_df <- bind_rows(FDRcorrectedSCCA_df,
SCCA_5foldCV_df,
SCCA_PMA_df,
ordinary_SCCA_df)
dim(simulation_results_df)
# [1] 56000 16
table((simulation_results_df$lambda_X == simulation_results_df$lambda_Y) |
(is.na(simulation_results_df$lambda_X) & is.na(simulation_results_df$lambda_Y)))
#--- Estimated FDR
custom_cols = c("FDR-corrected SCCA (q = 0.2)" = "#a50026",
"FDR-corrected SCCA (q = 0.1)" = "#d73027",
"FDR-corrected SCCA (q = 0.05)" = "#fdae61",
"L1 SCCA: 5-fold CV" = "#4575b4",
"L1 SCCA: permutation-based" = "#c51b7d",
"L1 SCCA: fixed penalty (0.3)" = "#91cf60",
"L1 SCCA: fixed penalty (0.01)" = "#1a9850")
custom_line_size = c("FDR-corrected SCCA (q = 0.2)" = 1,
"FDR-corrected SCCA (q = 0.1)" = 1,
"FDR-corrected SCCA (q = 0.05)" = 1,
"L1 SCCA: 5-fold CV" = 1/2,
"L1 SCCA: permutation-based" = 1/2,
"L1 SCCA: fixed penalty (0.3)" = 1/2,
"L1 SCCA: fixed penalty (0.01)" = 1/2)
custom_shape = c("FDR-corrected SCCA (q = 0.2)" = 1,
"FDR-corrected SCCA (q = 0.1)" = 1,
"FDR-corrected SCCA (q = 0.05)" = 1,
"L1 SCCA: 5-fold CV" = 2,
"L1 SCCA: permutation-based" = 3,
"L1 SCCA: fixed penalty (0.3)" = 4,
"L1 SCCA: fixed penalty (0.01)" = 5)
df_for_FDR_viz <- simulation_results_df %>%
select(method, n_signif_X, n_signif_Y, FDP_u, FDP_v) %>%
mutate(method = factor(method, levels = names(custom_cols))) %>%
# (put the method names in desired order)
gather(key = "u_or_v", value = "FDP", -starts_with("n_"), -method) %>%
mutate(u_or_v = gsub("FDP_(.)", "Estimated FDR(\\1)", u_or_v)) %>%
group_by(n_signif_X, n_signif_Y, method, u_or_v) %>%
summarize(FDR = mean(FDP), SE = sd(FDP) / sqrt(n()), n_repl = n()) %>%
tbl_df() %>%
select(n_signif_X, n_signif_Y, FDR, SE, method, u_or_v) %>%
mutate(lwr = FDR - 2*SE, upr = FDR + 2*SE)
# plot of FDR(u)
df_for_FDR_viz %>%
filter(u_or_v == "Estimated FDR(u)") %>%
mutate(n_signif_Y = factor(n_signif_Y,
levels = sort(unique(n_signif_Y)),
labels = paste0("s[Y]==", sort(unique(n_signif_Y))),
ordered = TRUE)) %>%
mutate(lwr = ifelse(lwr < 0, 0, lwr)) %>%
ggplot(aes(x = n_signif_X, y = FDR)) +
geom_segment(aes(x = 0, y = 0.05, xend = max(n_signif_X), yend = 0.05), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.1, xend = max(n_signif_X), yend = 0.1), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.2, xend = max(n_signif_X), yend = 0.2), lty = 2, color = "grey", lwd = 0.2) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
# geom_ribbon(aes(ymin = FDR - 2*SE, ymax = FDR + 2*SE), alpha = 0.3) +
geom_errorbar(aes(ymin = lwr, ymax = upr, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_y_sqrt(breaks = c(0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.00)) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_Y, nrow = 1,
labeller = labeller(n_signif_Y = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated FDR(u) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_FDR(u)_combined_all_methods_nolegend.pdf")
# plot of FDR(v)
df_for_FDR_viz %>%
filter(u_or_v == "Estimated FDR(v)") %>%
mutate(n_signif_X = factor(n_signif_X,
levels = sort(unique(n_signif_X)),
labels = paste0("s[X]==", sort(unique(n_signif_X))),
ordered = TRUE)) %>%
mutate(lwr = ifelse(lwr < 0, 0, lwr)) %>%
ggplot(aes(x = n_signif_Y, y = FDR)) +
geom_segment(aes(x = 0, y = 0.05, xend = max(n_signif_Y), yend = 0.05), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.1, xend = max(n_signif_Y), yend = 0.1), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.2, xend = max(n_signif_Y), yend = 0.2), lty = 2, color = "grey", lwd = 0.2) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
# geom_ribbon(aes(ymin = FDR - 2*SE, ymax = FDR + 2*SE), alpha = 0.3) +
geom_errorbar(aes(ymin = lwr, ymax = upr, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_y_sqrt(breaks = c(0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.00)) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_X, nrow = 1,
labeller = labeller(n_signif_X = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated FDR(v) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_FDR(v)_combined_all_methods_nolegend.pdf")
#--- Mean sensitivity
df_for_TPR_viz <- simulation_results_df %>%
select(method, n_signif_X, n_signif_Y, TP_u, TP_v) %>%
mutate(method = factor(method, levels = names(custom_cols))) %>%
# (put the method names in desired order)
gather(key = "u_or_v", value = "TP", -starts_with("n_"), -method) %>%
mutate(u_or_v = gsub("TP_(.)", "Estimated TPR(\\1)", u_or_v)) %>%
mutate(TPP = ifelse(u_or_v == "Estimated TPR(u)",
TP / n_signif_X,
TP / n_signif_Y)) %>%
group_by(n_signif_X, n_signif_Y, method, u_or_v) %>%
summarize(TPR = mean(TPP), SE = sd(TPP) / sqrt(n()), count = n()) %>%
tbl_df() %>%
select(n_signif_X, n_signif_Y, TPR, SE, method, u_or_v)
# plot of TPR(v)
df_for_TPR_viz %>%
filter(u_or_v == "Estimated TPR(v)") %>%
mutate(n_signif_X = factor(n_signif_X,
levels = sort(unique(n_signif_X)),
labels = paste0("s[X]==", sort(unique(n_signif_X))),
ordered = TRUE)) %>%
ggplot(aes(x = n_signif_Y, y = TPR)) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
geom_errorbar(aes(ymin = TPR - 2*SE, ymax = TPR + 2*SE, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_X, nrow = 1,
labeller = labeller(n_signif_X = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated TPR(v) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_TPR(v)_combined_all_methods_nolegend.pdf")
# plot of TPR(u)
df_for_TPR_viz %>%
filter(u_or_v == "Estimated TPR(u)") %>%
mutate(n_signif_Y = factor(n_signif_Y,
levels = sort(unique(n_signif_Y)),
labels = paste0("s[Y]==", sort(unique(n_signif_Y))),
ordered = TRUE)) %>%
ggplot(aes(x = n_signif_X, y = TPR)) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
geom_errorbar(aes(ymin = TPR - 2*SE, ymax = TPR + 2*SE, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_color_manual(values = custom_cols, guide = guide_legend(nrow = 3)) +
scale_size_manual(values = custom_line_size, guide = guide_legend(nrow = 3)) +
scale_shape_manual(values = custom_shape, guide = guide_legend(nrow = 3)) +
facet_wrap(~n_signif_Y, nrow = 1,
labeller = labeller(n_signif_Y = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated TPR(u) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.key.width = unit(3, "lines"))
# legend.background = element_rect(color = "black", size = 0.2))
ggsave("./viz/low_dim_TPR(u)_combined_all_methods_legend.pdf")
agisga/FDRcorrectedSCCA documentation built on May 20, 2019, 6:45 p.m.
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library(tidyverse)
load_csv_results <- function(subdir) {
results_dir <- "./results/"
if (!grepl(".+/$", subdir)) { subdir <- paste0(subdir, "/") }
subdir_full_path <- paste0(results_dir, subdir)
csv_results <- list.files(subdir_full_path)
# keep csv files only
csv_results <- csv_results[grepl("^.+\\.csv$", csv_results)]
csv_results <- paste0(subdir_full_path, csv_results)
results_df <- NULL
for (filename in csv_results) {
tmp_df <- read_csv(filename, guess_max = 1e6)
if ( !("iter" %in% colnames(tmp_df)) ) {
warning(paste("'iter' not recorded for file", filename))
tmp_df$iter <- 1:nrow(tmp_df)
}
# (setting guess_max to avoid readr from guessing that a column is integer when it is double)
if (is.null(results_df)) {
results_df <- tmp_df
} else {
results_df <- bind_rows(results_df, tmp_df)
}
}
return(results_df)
}
FDRcorrectedSCCA_df <- load_csv_results("constant_low_dimensional_FDRcorrectedSCCA")
SCCA_5foldCV_df <- load_csv_results("constant_low_dimensional_5foldCV")
SCCA_PMA_df <- load_csv_results("constant_low_dimensional_PMA")
ordinary_SCCA_df <- load_csv_results("constant_low_dimensional_ordinary_SCCA")
dim(FDRcorrectedSCCA_df)
# [1] 24000 13
dim(SCCA_5foldCV_df)
# [1] 8000 14
dim(SCCA_PMA_df)
# [1] 8000 14
dim(ordinary_SCCA_df)
# [1] 16000 14
colnames(FDRcorrectedSCCA_df)
colnames(SCCA_5foldCV_df)
colnames(SCCA_PMA_df)
colnames(ordinary_SCCA_df)
FDRcorrectedSCCA_df <- mutate(FDRcorrectedSCCA_df, method = "FDR-corrected SCCA") %>%
mutate(method = paste0(method, " (q = ", target_FDR, ")"))
SCCA_5foldCV_df <- mutate(SCCA_5foldCV_df, method = "L1 SCCA: 5-fold CV")
SCCA_PMA_df <- mutate(SCCA_PMA_df, method = "L1 SCCA: permutation-based")
ordinary_SCCA_df <- mutate(ordinary_SCCA_df, method = "L1 SCCA: fixed penalty") %>%
mutate(method = paste0(method, " (", lambda_X, ")"))
simulation_results_df <- bind_rows(FDRcorrectedSCCA_df,
SCCA_5foldCV_df,
SCCA_PMA_df,
ordinary_SCCA_df)
dim(simulation_results_df)
# [1] 56000 16
table((simulation_results_df$lambda_X == simulation_results_df$lambda_Y) |
(is.na(simulation_results_df$lambda_X) & is.na(simulation_results_df$lambda_Y)))
#--- Estimated FDR
custom_cols = c("FDR-corrected SCCA (q = 0.2)" = "#a50026",
"FDR-corrected SCCA (q = 0.1)" = "#d73027",
"FDR-corrected SCCA (q = 0.05)" = "#fdae61",
"L1 SCCA: 5-fold CV" = "#4575b4",
"L1 SCCA: permutation-based" = "#c51b7d",
"L1 SCCA: fixed penalty (0.3)" = "#91cf60",
"L1 SCCA: fixed penalty (0.01)" = "#1a9850")
custom_line_size = c("FDR-corrected SCCA (q = 0.2)" = 1,
"FDR-corrected SCCA (q = 0.1)" = 1,
"FDR-corrected SCCA (q = 0.05)" = 1,
"L1 SCCA: 5-fold CV" = 1/2,
"L1 SCCA: permutation-based" = 1/2,
"L1 SCCA: fixed penalty (0.3)" = 1/2,
"L1 SCCA: fixed penalty (0.01)" = 1/2)
custom_shape = c("FDR-corrected SCCA (q = 0.2)" = 1,
"FDR-corrected SCCA (q = 0.1)" = 1,
"FDR-corrected SCCA (q = 0.05)" = 1,
"L1 SCCA: 5-fold CV" = 2,
"L1 SCCA: permutation-based" = 3,
"L1 SCCA: fixed penalty (0.3)" = 4,
"L1 SCCA: fixed penalty (0.01)" = 5)
df_for_FDR_viz <- simulation_results_df %>%
select(method, n_signif_X, n_signif_Y, FDP_u, FDP_v) %>%
mutate(method = factor(method, levels = names(custom_cols))) %>%
# (put the method names in desired order)
gather(key = "u_or_v", value = "FDP", -starts_with("n_"), -method) %>%
mutate(u_or_v = gsub("FDP_(.)", "Estimated FDR(\\1)", u_or_v)) %>%
group_by(n_signif_X, n_signif_Y, method, u_or_v) %>%
summarize(FDR = mean(FDP), SE = sd(FDP) / sqrt(n()), n_repl = n()) %>%
tbl_df() %>%
select(n_signif_X, n_signif_Y, FDR, SE, method, u_or_v) %>%
mutate(lwr = FDR - 2*SE, upr = FDR + 2*SE)
# plot of FDR(u)
df_for_FDR_viz %>%
filter(u_or_v == "Estimated FDR(u)") %>%
mutate(n_signif_Y = factor(n_signif_Y,
levels = sort(unique(n_signif_Y)),
labels = paste0("s[Y]==", sort(unique(n_signif_Y))),
ordered = TRUE)) %>%
mutate(lwr = ifelse(lwr < 0, 0, lwr)) %>%
ggplot(aes(x = n_signif_X, y = FDR)) +
geom_segment(aes(x = 0, y = 0.05, xend = max(n_signif_X), yend = 0.05), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.1, xend = max(n_signif_X), yend = 0.1), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.2, xend = max(n_signif_X), yend = 0.2), lty = 2, color = "grey", lwd = 0.2) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
# geom_ribbon(aes(ymin = FDR - 2*SE, ymax = FDR + 2*SE), alpha = 0.3) +
geom_errorbar(aes(ymin = lwr, ymax = upr, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_y_sqrt(breaks = c(0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.00)) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_Y, nrow = 1,
labeller = labeller(n_signif_Y = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated FDR(u) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_FDR(u)_combined_all_methods_nolegend.pdf")
# plot of FDR(v)
df_for_FDR_viz %>%
filter(u_or_v == "Estimated FDR(v)") %>%
mutate(n_signif_X = factor(n_signif_X,
levels = sort(unique(n_signif_X)),
labels = paste0("s[X]==", sort(unique(n_signif_X))),
ordered = TRUE)) %>%
mutate(lwr = ifelse(lwr < 0, 0, lwr)) %>%
ggplot(aes(x = n_signif_Y, y = FDR)) +
geom_segment(aes(x = 0, y = 0.05, xend = max(n_signif_Y), yend = 0.05), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.1, xend = max(n_signif_Y), yend = 0.1), lty = 2, color = "grey", lwd = 0.2) +
geom_segment(aes(x = 0, y = 0.2, xend = max(n_signif_Y), yend = 0.2), lty = 2, color = "grey", lwd = 0.2) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
# geom_ribbon(aes(ymin = FDR - 2*SE, ymax = FDR + 2*SE), alpha = 0.3) +
geom_errorbar(aes(ymin = lwr, ymax = upr, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_y_sqrt(breaks = c(0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.00)) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_X, nrow = 1,
labeller = labeller(n_signif_X = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated FDR(v) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_FDR(v)_combined_all_methods_nolegend.pdf")
#--- Mean sensitivity
df_for_TPR_viz <- simulation_results_df %>%
select(method, n_signif_X, n_signif_Y, TP_u, TP_v) %>%
mutate(method = factor(method, levels = names(custom_cols))) %>%
# (put the method names in desired order)
gather(key = "u_or_v", value = "TP", -starts_with("n_"), -method) %>%
mutate(u_or_v = gsub("TP_(.)", "Estimated TPR(\\1)", u_or_v)) %>%
mutate(TPP = ifelse(u_or_v == "Estimated TPR(u)",
TP / n_signif_X,
TP / n_signif_Y)) %>%
group_by(n_signif_X, n_signif_Y, method, u_or_v) %>%
summarize(TPR = mean(TPP), SE = sd(TPP) / sqrt(n()), count = n()) %>%
tbl_df() %>%
select(n_signif_X, n_signif_Y, TPR, SE, method, u_or_v)
# plot of TPR(v)
df_for_TPR_viz %>%
filter(u_or_v == "Estimated TPR(v)") %>%
mutate(n_signif_X = factor(n_signif_X,
levels = sort(unique(n_signif_X)),
labels = paste0("s[X]==", sort(unique(n_signif_X))),
ordered = TRUE)) %>%
ggplot(aes(x = n_signif_Y, y = TPR)) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
geom_errorbar(aes(ymin = TPR - 2*SE, ymax = TPR + 2*SE, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_color_manual(values = custom_cols) +
scale_size_manual(values = custom_line_size) +
scale_shape_manual(values = custom_shape) +
facet_wrap(~n_signif_X, nrow = 1,
labeller = labeller(n_signif_X = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated TPR(v) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "none")
ggsave("./viz/low_dim_TPR(v)_combined_all_methods_nolegend.pdf")
# plot of TPR(u)
df_for_TPR_viz %>%
filter(u_or_v == "Estimated TPR(u)") %>%
mutate(n_signif_Y = factor(n_signif_Y,
levels = sort(unique(n_signif_Y)),
labels = paste0("s[Y]==", sort(unique(n_signif_Y))),
ordered = TRUE)) %>%
ggplot(aes(x = n_signif_X, y = TPR)) +
geom_line(aes(color = method, linetype = method, size = method)) +
geom_point(aes(color = method, shape = method)) +
geom_errorbar(aes(ymin = TPR - 2*SE, ymax = TPR + 2*SE, color = method),
width = 2, size = 0.4, show.legend = FALSE) +
scale_color_manual(values = custom_cols, guide = guide_legend(nrow = 3)) +
scale_size_manual(values = custom_line_size, guide = guide_legend(nrow = 3)) +
scale_shape_manual(values = custom_shape, guide = guide_legend(nrow = 3)) +
facet_wrap(~n_signif_Y, nrow = 1,
labeller = labeller(n_signif_Y = label_parsed, n_col = label_parsed)) +
xlab(expression(s[Y])) + ylab("Estimated TPR(u) +/- 2SE") +
theme_bw()+
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.key.width = unit(3, "lines"))
# legend.background = element_rect(color = "black", size = 0.2))
ggsave("./viz/low_dim_TPR(u)_combined_all_methods_legend.pdf")
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