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library(ggplot2); library(cowplot);
## function for color
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}res_df = readRDS("./results/scenario11.RDS")
method_list = c("Mr.ASH","varbvs","BayesB","B-Lasso","SuSiE","E-NET","Lasso",
"Ridge","SCAD.2tuning","MCP.2tuning","SCAD","MCP","L0Learn","Mr.ASH.null")
method_level = c("Mr.ASH","E-NET","Lasso","Ridge","SCAD","MCP","L0Learn",
"varbvs","BayesB","B-Lasso","SuSiE","SCAD.2tuning","MCP.2tuning","Mr.ASH.null")
df = data.frame()
p_range = c(20,50,100,200,500,1000,2000,5000,10000,20000)
for (i in 1:10) {
res_df[[i]]$fit = rep(method_list, each = 20)
res_df[[i]]$fit = factor(res_df[[i]]$fit, levels = method_list)
df = rbind(df, data.frame(pred = c(colMeans(matrix(res_df[[i]]$pred, 20, 14))),
time2 = apply(matrix(res_df[[i]]$time, 20, 14), 2, median),
time = c(colMeans(matrix(res_df[[i]]$time, 20, 14))),
p = p_range[i],
fit = method_list))
}
df$fit = factor(df$fit, levels = method_level)
df = df[df$fit %in% c("Mr.ASH","E-NET","Lasso","Ridge","SCAD","MCP","L0Learn","Mr.ASH.null"),]
df = df[df$p %in% c(20,50,200,500,1000,2000,10000,20000),]
df$size = 0.5
df$size[1:8] = 1.2
col = gg_color_hue(13)[c(1:7,13)]
df$lt = "solid"
#df$lt[57:72] = "dashed"
shape = c(19,17,24,25,9,3,11,4,5,7,8,10,1)[c(1:7,13)]
p1 = ggplot(df) + geom_line(aes(x = p, y = pred, color = fit), size = df$size, linetype = df$lt) +
geom_point(aes(x = p, y = pred, color = fit, shape = fit), size = 2.5) +
theme_cowplot(font_size = 22) +
scale_x_continuous(trans = "log10", breaks = c(20,50,200,500,1000,2000,10000,20000)) +
labs(y = "predictior error (rmse / sigma)", x = "number of predictors (p)") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,hjust = 1),
axis.title.y = element_blank()) +
scale_color_manual(values = col) +
scale_shape_manual(values = shape) +
scale_y_continuous(trans = "log10", breaks = c(1,1.1,1.2,1.3,1.4)) +
coord_cartesian(ylim = c(1,1.3))
fig_main = p1 + theme(legend.position = "none")
suptitle = ggdraw() + draw_label("Varying p", fontface = 'bold.italic', size = 14)
subtitle = ggdraw() + draw_label("Scenario: IndepGauss + PointNormal, n = 500, p = 20-20000, s = 20, pve = 0.5", fontface = 'bold', size = 14)
fig11_1 = plot_grid(fig_main, ncol = 1, rel_heights = c(0.05,0.95))res_df = readRDS("./results/scenario11.RDS")
df = data.frame()
p_range = c(20,50,100,200,500,1000,2000,5000,10000,20000)
for (i in 1:10) {
res_df[[i]]$fit = rep(method_list, each = 20)
res_df[[i]]$fit = factor(res_df[[i]]$fit, levels = method_list)
df = rbind(df, data.frame(pred = c(colMeans(matrix(res_df[[i]]$pred, 20, 14))),
time2 = apply(matrix(res_df[[i]]$time, 20, 14), 2, median),
time = c(colMeans(matrix(res_df[[i]]$time, 20, 14))),
p = p_range[i],
fit = method_list))
}
df$fit = factor(df$fit, levels = method_level)
df = df[df$fit %in% c("Mr.ASH","E-NET","Lasso","Ridge","SCAD",
"MCP","L0Learn","Mr.ASH.null"),]
df$size = 0.5
df$size[1:5] = 1.2; #df$size[36:40] = 1.2;
df$size[41:45] = 1.2;
col = gg_color_hue(13)[c(1:7,13)]
df$lt = "solid"
#df$lt[c(36:45,81:90)] = "dashed"
shape = c(19,17,24,25,9,3,11,4,5,7,8,10,1)[c(1:7,13)]
p1 = ggplot() + geom_line(aes(x = df$p[df$p <= 500], y = df$time[df$p <= 500], color = df$fit[df$p <= 500]),
size = df$size[df$p <= 500], linetype = df$lt[df$p <= 500]) +
geom_point(aes(x = df$p[df$p <= 500], y = df$time[df$p <= 500], color = df$fit[df$p <= 500],
shape = df$fit[df$p <= 500]), size = 2.5) +
scale_color_manual(values = col) +
scale_shape_manual(values = shape)
fig_main = p1 + geom_line(aes(x = df$p[df$p > 500], y = df$time[df$p > 500], color = df$fit[df$p > 500]),
size = df$size[df$p > 500], linetype = df$lt[df$p > 500]) +
geom_point(aes(x = df$p[df$p > 500], y = df$time[df$p > 500], color = df$fit[df$p > 500],
shape = df$fit[df$p > 500]), size = 2.5) +
theme_cowplot(font_size = 22) +
scale_x_continuous(trans = "log10", breaks = p_range) +
labs(y = "computation time (seconds)", x = "number of nonzero coefficients (s)") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust = 0.5),
legend.position = "none",
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,hjust = 1),
axis.title.y = element_blank()) +
scale_y_continuous(trans = "log10", breaks = c(0.01,0.1,1,10,100)) +
coord_cartesian(ylim = c(0.01,300))
subtitle = ggdraw() + draw_label("Computation Time (seconds)", fontface = 'bold', size = 14)
fig11_2 = plot_grid(fig_main, ncol = 1, rel_heights = c(0.05,0.95))res_df = readRDS("./results/scenario11.RDS")
df = data.frame()
p_range = c(20,50,100,200,500,1000,2000,5000,10000,20000)
for (i in 1:10) {
res_df[[i]]$fit = rep(method_list, each = 20)
res_df[[i]]$fit = factor(res_df[[i]]$fit, levels = method_list)
df = rbind(df, data.frame(pred = c(colMeans(matrix(res_df[[i]]$pred, 20, 14))),
time2 = apply(matrix(res_df[[i]]$time, 20, 14), 2, median),
time = c(colMeans(matrix(res_df[[i]]$time, 20, 14))),
p = p_range[i],
fit = method_list))
}
df$fit = factor(df$fit, levels = method_level)
df = df[df$fit %in% c("Mr.ASH","varbvs","BayesB","B-Lasso","SuSiE","E-NET","Lasso",
"Ridge","SCAD","MCP","L0Learn","Mr.ASH.null"),]
df = df[df$p %in% c(20,50,200,500,1000,2000,10000,20000),]
df$size = 0.5
df$size[1:8] = 1.2
col = gg_color_hue(13)[c(1:11,13)]
df$lt = "solid"
shape = c(19,17,24,25,9,3,11,4,5,7,8,10,1)[c(1:11,13)]
#df$lt[89:104] = "dashed"
df$Method = df$fit
fig_dummy4 = ggplot(df) + geom_line(aes(x = p, y = pred, color = Method), size = df$size, linetype = df$lt) +
geom_point(aes(x = p, y = pred, color = Method, shape = Method), size = 2.5) +
theme_cowplot(font_size = 22) +
scale_x_continuous(trans = "log10", breaks = c(20,50,200,500,1000,2000,10000,20000)) +
labs(y = "predictior error (rmse / sigma)", x = "number of predictors (p)") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,hjust = 1),
axis.title.y = element_blank()) +
scale_color_manual(values = col) +
scale_shape_manual(values = shape) +
scale_y_continuous(trans = "log10", breaks = c(1,1.1,1.2,1.3,1.4)) +
coord_cartesian(ylim = c(1,1.3))res_df = readRDS("./results/scenario11.RDS")
df = data.frame()
p_range = c(20,50,100,200,500,1000,2000,5000,10000,20000)
for (i in 1:10) {
res_df[[i]]$fit = rep(method_list, each = 20)
res_df[[i]]$fit = factor(res_df[[i]]$fit, levels = method_list)
df = rbind(df, data.frame(pred = c(colMeans(matrix(res_df[[i]]$pred, 20, 14))),
time2 = apply(matrix(res_df[[i]]$time, 20, 14), 2, median),
time = c(colMeans(matrix(res_df[[i]]$time, 20, 14))),
p = p_range[i],
fit = method_list))
}
df$fit = factor(df$fit, levels = method_level)
df = df[df$p %in% c(20,50,200,500,1000,2000,10000,20000),]
df = df[df$fit %in% c("Mr.ASH","varbvs","BayesB","B-Lasso","SuSiE","Mr.ASH.null"),]
df$size = 0.5
df$size[1:8] = 1.2
col = gg_color_hue(13)[c(1,8:11,13)]
df$lt = "solid"
shape = c(19,17,24,25,9,3,11,4,5,7,8,10,1)[c(1,8:11,13)]
p1 = ggplot(df) + geom_line(aes(x = p, y = pred, color = fit), size = df$size, linetype = df$lt) +
geom_point(aes(x = p, y = pred, color = fit, shape = fit), size = 2.5) +
theme_cowplot(font_size = 22) +
scale_x_continuous(trans = "log10", breaks = c(20,50,200,500,1000,2000,10000,20000)) +
labs(y = "predictior error (rmse / sigma)", x = "number of predictors (p)") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust = 0.5),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,hjust = 1),
axis.title.y = element_blank()) +
scale_color_manual(values = col) +
scale_shape_manual(values = shape) +
scale_y_continuous(trans = "log10", breaks = c(1,1.1,1.2,1.3,1.4)) +
coord_cartesian(ylim = c(1,1.3))
fig_main = p1 + theme(legend.position = "none")
# suptitle = ggdraw() + draw_label("Varying p", fontface = 'bold', size = 14)
# subtitle = ggdraw() + draw_label("Scenario: IndepGauss + PointNormal, n = 500, p = 20-20000, s = 20, pve = 0.5", fontface = 'bold', size = 14)
fig11_3 = plot_grid(fig_main, ncol = 1, rel_heights = c(0.05,0.95))res_df = readRDS("./results/scenario11.RDS")
df = data.frame()
p_range = c(20,50,100,200,500,1000,2000,5000,10000,20000)
for (i in 1:10) {
res_df[[i]]$fit = rep(method_list, each = 20)
res_df[[i]]$fit = factor(res_df[[i]]$fit, levels = method_list)
df = rbind(df, data.frame(pred = c(colMeans(matrix(res_df[[i]]$pred, 20, 14))),
time2 = apply(matrix(res_df[[i]]$time, 20, 14), 2, median),
time = c(colMeans(matrix(res_df[[i]]$time, 20, 14))),
p = p_range[i],
fit = method_list))
}
df$fit = factor(df$fit, levels = method_level)
df = df[df$fit %in% c("Mr.ASH","varbvs","BayesB","B-Lasso","SuSiE","Mr.ASH.null"),]
df$size = 0.5
df$size[1:9] = 1.2
col = gg_color_hue(13)[c(1,8:11,13)]
shape = c(19,17,24,25,9,3,11,4,5,7,8,10,1)[c(1,8:11,13)]
fig_main = ggplot(df) + geom_line(aes(x = p, y = time, color = fit),
size = df$size) +
geom_point(aes(x = p, y = time, color = fit, shape = fit), size = 2.5) +
theme_cowplot(font_size = 22) +
scale_x_continuous(trans = "log10", breaks = p_range) +
labs(y = "computation time (seconds)", x = "number of nonzero coefficients (s)") +
theme(axis.line = element_blank(),
plot.title = element_text(hjust = 0.5),
legend.position = "none",
axis.text.x = element_text(angle = 45,hjust = 1),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
scale_y_continuous(trans = "log10", breaks = c(0.01,0.1,1,10,100), limits = c(0.01,300)) +
scale_color_manual(values = col) +
scale_shape_manual(values = shape)
#subtitle = ggdraw() + draw_label("Computation Time (seconds)", fontface = 'bold', size = 14)
fig11_4 = plot_grid(fig_main, ncol = 1, rel_heights = c(0.05,0.95))yaxis1 = ggdraw() + draw_label("prediction error (rmse / sigma)", size = 25, angle = 90)
yaxis2 = ggdraw() + draw_label("computation time (seconds)", size = 25, angle = 90)
blank1 = ggplot() + geom_blank() + theme_cowplot() + theme(axis.line = element_blank())
figure6 = plot_grid(yaxis1, fig11_1, fig11_3,
blank1, blank1, blank1, yaxis2, fig11_2, fig11_4, ncol = 3, rel_widths = c(0.03,0.5,0.5),
rel_heights = c(0.5,0.05,0.5))
# title = ggdraw() + draw_label("Varying p (Scenario 11)", fontface = 'bold', size = 24)
title = ggdraw() + draw_label("Varying p", fontface = 'bold.italic', size = 24)
legend <- get_legend(fig_dummy4 +
theme_cowplot(font_size = 22) +
theme(legend.box.margin = margin(0, 0, 0, 12),
legend.key.size = unit(0.8, "cm")))
xaxis = ggdraw() + draw_label("number of predictors (p)", size = 25, angle = 0)
figure6 = plot_grid(plot_grid(title, blank1, plot_grid(figure6, xaxis, rel_heights = c(0.97,0.03), nrow = 2),
ncol = 1, rel_heights = c(0.05, 0.02, 0.95)),
legend, nrow = 1, rel_widths = c(0.9,0.15))
ggsave("./figure6_for_paper.pdf", figure6, width = 18, height = 17)
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.0.0 ggplot2_3.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 compiler_3.5.3 pillar_1.4.3 git2r_0.26.1
[5] workflowr_1.4.0 tools_3.5.3 digest_0.6.23 evaluate_0.14
[9] lifecycle_0.1.0 tibble_2.1.3 gtable_0.3.0 pkgconfig_2.0.3
[13] rlang_0.4.2 yaml_2.2.0 xfun_0.9 withr_2.1.2
[17] stringr_1.4.0 dplyr_0.8.3 knitr_1.25 fs_1.3.1
[21] rprojroot_1.3-2 grid_3.5.3 tidyselect_0.2.5 glue_1.3.1
[25] R6_2.4.1 rmarkdown_1.15 purrr_0.3.2 farver_2.0.3
[29] magrittr_1.5 whisker_0.4 backports_1.1.5 scales_1.1.0
[33] htmltools_0.3.6 assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.5
[37] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4