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06_figure3_small_simulation.Rmd
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06_figure3_small_simulation.Rmd
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---
title: "06_figure3_small_simulation"
author: "randy"
date: "`r Sys.Date()`"
output:
pdf_document: default
html_document: default
---
The simulation study with 500 samples and 400 in training set and 100 in testing set
```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE,
# warnings = FALSE,
# message = FALSE,
# comment = "#>",
# #results = "hide",
# digits = 4,
# error = FALSE)
## clean the R environment
graphics.off()
rm(list = ls())
freshr::freshr()
## load packages
library(here, quietly = TRUE)
library(tidyverse, quietly = TRUE)
library(gtsummary, quietly = TRUE)
library(flextable, quietly = TRUE)
## check the directory for the file
# here::dr_here()
here::set_here()
## the figure or results should be saved
# paste0("foldername/Sfilename_workingresult_",
#. Sys.Date(), ".filetype")
```
```{r eval=FALSE, include=FALSE}
## 7.1 meanout -----------------------------------------------------------------
meanout <- function(dataset,
term = c("bias", "mse", "coverage50",
"coverage80", "coverage90"),
...){
result <- dataset %>%
map("centiles_observed")
result <- keep(result, ~ length(.x) > 1) %>%
map(~mutate(., bias = abs(`50` - actual),
coverage50 = ifelse(actual >= `25` & actual <= `75`, 1, 0),
coverage80 = ifelse(actual >= `10` & actual <= `90`, 1, 0),
coverage90 = ifelse(actual >= `5` & actual <= `95`, 1, 0)))
if (term != "mse") {
result <- result %>%
map(term) %>%
map(~mean(., na.rm = TRUE)) %>%
unlist() %>%
mean()
} else {
result <- result %>%
map(~.$bias ^2) %>%
map(~mean(., na.rm = TRUE)) %>%
unlist() %>%
mean()
}
return(result)
}
## 7.2 meanout -----------------------------------------------------------------
meanall <- function(dataset,
...){
bias <- meanout(dataset, "bias")
mse <- meanout(dataset, "mse")
cov50 <- meanout(dataset, "coverage50")
cov80 <- meanout(dataset, "coverage80")
cov90 <- meanout(dataset, "coverage90")
return(list(bias = bias,
mse = mse,
cov50 = cov50,
cov80 = cov80,
cov90 = cov90))
}
pull_simulation <- function(location,
filename) {
load(paste0(location, filename))
## eld_n test {{{-------------------------------------------------------------
eld_n <- meanall(eld_n10)
## mhl_n test {{{-------------------------------------------------------------
mhl_n <- meanall(mhl_n10)
## mhl_p test {{{-------------------------------------------------------------
mhl_p75 <- try(meanall(mhl_p075))
mhl_p80 <- try(meanall(mhl_p080))
mhl_p85 <- try(meanall(mhl_p085))
mhl_p90 <- try(meanall(mhl_p090))
mhl_p95 <- try(meanall(mhl_p095))
## for the large simulation we used test_mhl_p09
# mhl_p <- meanall(test_mhl_p09)
## sgl_n test {{{-------------------------------------------------------------
sgl_n <- meanall(sgl_n10)
if (exists("lmm_test")) {
lmm <- list()
lmm$bias <- lmm_test %>%
transmute(bias = abs(pred - ht)) %>%
unlist() %>%
mean()
lmm$mse <- lmm_test %>%
transmute(mse = (pred - ht)^2) %>%
unlist() %>%
mean()
## rmse is the sqrt
lmm$cov50<- lmm_test %>%
transmute(ifelse(ht >= centile25 & ht <= centile75, 1, 0)) %>%
unlist() %>%
mean()
lmm$cov80<- lmm_test %>%
transmute(ifelse(ht >= centile10 & ht <= centile90, 1, 0)) %>%
unlist() %>%
mean()
lmm$cov90<- lmm_test %>%
transmute(ifelse(ht >= centile05 & ht <= centile95, 1, 0)) %>%
unlist() %>%
mean()
} else {
lmm = NULL
}
return(list(eld_n = eld_n,
mhl_n = mhl_n,
mhl_p75 = mhl_p75,
mhl_p80 = mhl_p80,
mhl_p85 = mhl_p85,
mhl_p90 = mhl_p90,
mhl_p95 = mhl_p95,
sgl_n = sgl_n,
lmm = lmm))
}
```
```{r}
## remember to add the / at the end of the location /
## there are two setup for simulation
## L simulation with 900 individuals ------------------
## S simulation with 500 individuals -----------------
## with 400 for training set; 100 for testing set
folder1 <- "result_ss500_simulation"
## for each simulation sample size (large or small)
## each has three setup with different anchor time set
## 3 anchor time setting for (6, 9, 12)
## 4 anchor time setting for (3, 6, 9, 12)
## 6 anchor time setting for (3, 6, 9, 12, 13, 15)
# folder2 <- "sim1000_ss500_anchor3time"
# folder2 <- "sim1000_ss500_anchor4time"
folder2 <- "sim1000_ss500_anchor6time"
# file_location <- paste0("results/", folder1, "/", folder2, "/")
# files <- list.files(path = file_location, pattern = ".Rdata")
# ## pull out the simulation results from the folder each as a list of
# ## bias, rmse, coverage50, coverage80, and coverage90
# sim_ss <- map_dfr(files, ~pull_simulation(file_location, .),
# .progress = list(type = "iterator",
# format = "Calculating {cli::pb_bar} {cli::pb_percent}",
# clear = TRUE))
# sim_ss <- sim_ss %>%
# mutate(term = rep(c("bias", "mse", "coverage50", "coverage80", "coverage90"), length(files)))
# save(sim_ss, file = paste0("data/S06_", folder2, "_0summary_", Sys.Date(), ".Rdata"))
```
```{r}
## There will be three Rdata files saved in the data folder
#
# load(paste0("data/S06_", folder2, "_0summary_", Sys.Date(), ".Rdata"))
load("~/Desktop/paper2023/data/S06_sim1000_ss500_anchor3time_0summary_2023-08-31.Rdata")
data <- sim_ss500 %>%
pivot_longer(cols = -term,
names_to = "method",
values_to = "values") %>%
mutate(method = factor(method, levels = c("sgl_n", "eld_n", "mhl_n",
"mhl_p75", "mhl_p80", "mhl_p85",
"mhl_p90", "mhl_p95", "lmm")))
library(latex2exp)
bias_plot <- data %>%
filter(term == "bias") %>%
ggplot(aes(x = method, y = as.numeric(values), group = factor(method))) +
scale_x_discrete(labels = c("sgl_n" = parse(text = latex2exp::TeX("$S_{\\kappa = 10}$")),
"eld_n" = parse(text = latex2exp::TeX("$E_{\\kappa = 10}$")),
"mhl_n" = parse(text = latex2exp::TeX("$M_{\\kappa = 10}$")),
"mhl_p75" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.75}$")),
"mhl_p80" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.80}$")),
"mhl_p85" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.85}$")),
"mhl_p90" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.90}$")),
"mhl_p95" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.95}$")),
"lmm" = "LMM")) +
geom_point(aes(color = method)) +
geom_boxplot(aes(fill = method)) +
theme_bw() +
labs(x = NULL) +
labs(y = "Bias") +
ggthemes::scale_fill_tableau("Jewel Bright") +
## so far the best color composition
ggthemes::scale_colour_tableau("Jewel Bright") +
## facet_wrap("group") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
axis.title.y = element_text(size = 16)) +
ylim(c(1.5, 2.3))
bias_plot
```
```{r}
rmse_plot <- data %>%
filter(term == "mse") %>%
mutate(values = sqrt(as.numeric(values))) %>%
ggplot(aes(x = method, y = as.numeric(values), group = method)) +
geom_point(aes(color = method)) +
geom_boxplot(aes(fill = method)) +
theme_bw() +
labs(x = NULL) +
labs(y = "RMSE") +
ggthemes::scale_fill_tableau("Jewel Bright") +
## so far the best color composition
ggthemes::scale_colour_tableau("Jewel Bright") +
## facet_wrap("group") +
theme(legend.position = "none") +
scale_x_discrete(labels = c("sgl_n" = parse(text = latex2exp::TeX("$S_{\\kappa = 10}$")),
"eld_n" = parse(text = latex2exp::TeX("$E_{\\kappa = 10}$")),
"mhl_n" = parse(text = latex2exp::TeX("$M_{\\kappa = 10}$")),
"mhl_p75" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.75}$")),
"mhl_p80" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.80}$")),
"mhl_p85" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.85}$")),
"mhl_p90" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.90}$")),
"mhl_p95" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.95}$")),
"lmm" = "LMM")) +
theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
axis.title.y = element_text(size = 16)) +
ylim(c(2, 4))
rmse_plot
```
```{r}
cov50_plot <- data %>%
filter(term == "coverage50") %>%
ggplot(aes(x = method, y = as.numeric(values)),
group = method) +
geom_point(aes(color = method)) +
geom_boxplot(aes(fill = method)) +
theme_bw() +
labs(x = NULL) +
labs(y = "50% CR") +
ggthemes::scale_fill_tableau("Jewel Bright") +
## so far the best color composition
ggthemes::scale_colour_tableau("Jewel Bright") +
## facet_wrap("group") +
theme(legend.position = "none") +
scale_x_discrete(labels = c("sgl_n" = parse(text = latex2exp::TeX("$S_{\\kappa = 10}$")),
"eld_n" = parse(text = latex2exp::TeX("$E_{\\kappa = 10}$")),
"mhl_n" = parse(text = latex2exp::TeX("$M_{\\kappa = 10}$")),
"mhl_p75" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.75}$")),
"mhl_p80" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.80}$")),
"mhl_p85" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.85}$")),
"mhl_p90" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.90}$")),
"mhl_p95" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.95}$")),
"lmm" = "LMM")) +
theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
axis.title.y = element_text(size = 16))
## the tableau is in the ggthemes
cov50_plot
```
```{r}
cov80_plot <- data %>%
filter(term == "coverage80") %>%
ggplot(aes(x = method, y = as.numeric(values)),
group = method) +
geom_point(aes(color = method)) +
geom_boxplot(aes(fill = method)) +
theme_bw() +
labs(x = NULL) +
labs(y = "80% CR") +
ggthemes::scale_fill_tableau("Jewel Bright") +
## so far the best color composition
ggthemes::scale_colour_tableau("Jewel Bright") +
## facet_wrap("group") +
theme(legend.position = "none") +
scale_x_discrete(labels = c("sgl_n" = parse(text = latex2exp::TeX("$S_{\\kappa = 10}$")),
"eld_n" = parse(text = latex2exp::TeX("$E_{\\kappa = 10}$")),
"mhl_n" = parse(text = latex2exp::TeX("$M_{\\kappa = 10}$")),
"mhl_p75" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.75}$")),
"mhl_p80" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.80}$")),
"mhl_p85" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.85}$")),
"mhl_p90" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.90}$")),
"mhl_p95" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.95}$")),
"lmm" = "LMM")) +
theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
axis.title.y = element_text(size = 16))
## the tableau is in the ggthemes
cov80_plot
```
```{r}
cov90_plot <- data %>%
filter(term == "coverage90") %>%
ggplot(aes(x = method, y = as.numeric(values)),
group = method) +
geom_point(aes(color = method)) +
geom_boxplot(aes(fill = method)) +
theme_bw() +
labs(x = NULL) +
labs(y = "90% CR") +
ggthemes::scale_fill_tableau("Jewel Bright") +
## so far the best color composition
ggthemes::scale_colour_tableau("Jewel Bright") +
## facet_wrap("group") +
theme(legend.position = "none") +
scale_x_discrete(labels = c("sgl_n" = parse(text = latex2exp::TeX("$S_{\\kappa = 10}$")),
"eld_n" = parse(text = latex2exp::TeX("$E_{\\kappa = 10}$")),
"mhl_n" = parse(text = latex2exp::TeX("$M_{\\kappa = 10}$")),
"mhl_p75" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.75}$")),
"mhl_p80" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.80}$")),
"mhl_p85" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.85}$")),
"mhl_p90" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.90}$")),
"mhl_p95" = parse(text = latex2exp::TeX("$M_{\\alpha = 0.95}$")),
"lmm" = "LMM")) +
theme(axis.text.x = element_text(size = 15),
axis.text.y = element_text(size = 15),
axis.title.y = element_text(size = 16))
## the tableau is in the ggthemes
cov90_plot
```
```{r fig.width=10, fig.height=3, warning=FALSE}
bias_plot
ggsave(paste0("figure/S06_", folder2, "_1bias_", Sys.Date(), ".png"))
rmse_plot
ggsave(paste0("figure/S06_", folder2, "_2rmse_", Sys.Date(), ".png"))
cov50_plot +
geom_hline(yintercept = 0.5, linetype="dashed", color = "indianred")
ggsave(paste0("figure/S06_", folder2, "_3cov50_", Sys.Date(), ".png"))
cov80_plot +
geom_hline(yintercept = 0.8, linetype="dashed", color = "indianred")
ggsave(paste0("figure/S06_", folder2, "_4cov80_", Sys.Date(), ".png"))
cov90_plot +
geom_hline(yintercept = 0.9, linetype="dashed", color = "indianred")
ggsave(paste0("figure/S06_", folder2, "_5cov90_", Sys.Date(), ".png"))
```
```{r, fig.height= 10, fig.width=10}
library(ggpubr)
figure1 <- ggarrange(bias_plot,
# + theme(axis.text.x=element_blank()),
rmse_plot,
# + theme(axis.text.x=element_blank()),
# cov50_plot,
# + theme(axis.text.x=element_blank()),
cov90_plot +
geom_hline(yintercept = 0.9,
linetype = "dashed",
color = "indianred"),
# + theme(axis.text.x=element_blank()),
ncol = 1, nrow = 3)
figure1
ggsave(paste0("figure/S06_", folder2, "_6combine_", Sys.Date(), ".png"))
```
```{r, fig.height= 10, fig.width=10}
figure2 <- ggarrange(bias_plot,
# + theme(axis.text.x=element_blank()),
rmse_plot,
# + theme(axis.text.x=element_blank()),
cov50_plot +
geom_hline(yintercept = 0.5,
linetype = "dashed",
color = "indianred"),
cov80_plot +
geom_hline(yintercept = 0.8,
linetype = "dashed",
color = "indianred"),
# + theme(axis.text.x=element_blank()),
cov90_plot +
geom_hline(yintercept = 0.9,
linetype = "dashed",
color = "indianred"),
# + theme(axis.text.x=element_blank()),
ncol = 1, nrow = 5)
figure2
ggsave(paste0("figure/S06_", folder2, "_7all_", Sys.Date(), ".png"))
```
```{r, fig.height= 10, fig.width=10}
figure3 <- ggarrange(#bias_plot,
# + theme(axis.text.x=element_blank()),
#rmse_plot,
# + theme(axis.text.x=element_blank()),
cov50_plot +
geom_hline(yintercept = 0.5,
linetype = "dashed",
color = "indianred"),
cov80_plot +
geom_hline(yintercept = 0.8,
linetype = "dashed",
color = "indianred"),
# + theme(axis.text.x=element_blank()),
cov90_plot +
geom_hline(yintercept = 0.9,
linetype = "dashed",
color = "indianred"),
# + theme(axis.text.x=element_blank()),
ncol = 1, nrow = 3)
figure3
ggsave(paste0("figure/S06_", folder2, "_8cr_", Sys.Date(), ".png"))
```
```{r}
sessionInfo()
```