39. 森林圖繪制
清除當(dāng)前環(huán)境中的變量
rm(list=ls())
設(shè)置工作目錄
setwd("C:/Users/Dell/Desktop/R_Plots/39forest/")
使用survminer包中的ggforest函數(shù)繪制森林圖
require("survival")
## Loading required package: survival
library(survminer)
## Warning: package 'survminer' was built under R version 3.6.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.6.3
## Loading required package: ggpubr
## Loading required package: magrittr
# 查看內(nèi)置示例數(shù)據(jù)
head(colon)
## id study rx sex age obstruct perfor adhere nodes status differ
## 1 1 1 Lev+5FU 1 43 0 0 0 5 1 2
## 2 1 1 Lev+5FU 1 43 0 0 0 5 1 2
## 3 2 1 Lev+5FU 1 63 0 0 0 1 0 2
## 4 2 1 Lev+5FU 1 63 0 0 0 1 0 2
## 5 3 1 Obs 0 71 0 0 1 7 1 2
## 6 3 1 Obs 0 71 0 0 1 7 1 2
## extent surg node4 time etype
## 1 3 0 1 1521 2
## 2 3 0 1 968 1
## 3 3 0 0 3087 2
## 4 3 0 0 3087 1
## 5 2 0 1 963 2
## 6 2 0 1 542 1
# 構(gòu)建COX回歸比例風(fēng)險模型
model <- coxph( Surv(time, status) ~ sex + rx + adhere,
data = colon )
# 查看cox回歸模型結(jié)果
model
## Call:
## coxph(formula = Surv(time, status) ~ sex + rx + adhere, data = colon)
##
## coef exp(coef) se(coef) z p
## sex -0.04615 0.95490 0.06609 -0.698 0.484994
## rxLev -0.02724 0.97313 0.07690 -0.354 0.723211
## rxLev+5FU -0.43723 0.64582 0.08395 -5.208 1.91e-07
## adhere 0.29355 1.34118 0.08696 3.376 0.000736
##
## Likelihood ratio test=46.51 on 4 df, p=1.925e-09
## n= 1858, number of events= 920
# 使用ggforest()函數(shù)繪制基礎(chǔ)森林圖
ggforest(model)
image.png
# 將數(shù)據(jù)集中的變量設(shè)置成因子，添加標(biāo)簽進行分組
colon <- within(colon, {
sex <- factor(sex, labels = c("female", "male"))
differ <- factor(differ, labels = c("well", "moderate", "poor"))
extent <- factor(extent, labels = c("submuc.", "muscle", "serosa", "contig."))
})
head(colon)
## id study rx sex age obstruct perfor adhere nodes status differ
## 1 1 1 Lev+5FU male 43 0 0 0 5 1 moderate
## 2 1 1 Lev+5FU male 43 0 0 0 5 1 moderate
## 3 2 1 Lev+5FU male 63 0 0 0 1 0 moderate
## 4 2 1 Lev+5FU male 63 0 0 0 1 0 moderate
## 5 3 1 Obs female 71 0 0 1 7 1 moderate
## 6 3 1 Obs female 71 0 0 1 7 1 moderate
## extent surg node4 time etype
## 1 serosa 0 1 1521 2
## 2 serosa 0 1 968 1
## 3 serosa 0 0 3087 2
## 4 serosa 0 0 3087 1
## 5 muscle 0 1 963 2
## 6 muscle 0 1 542 1
# 使用coxph()函數(shù)進行COX回歸分析
bigmodel <- coxph(Surv(time, status) ~ sex + rx + adhere + differ + extent + node4,
data = colon )
bigmodel
## Call:
## coxph(formula = Surv(time, status) ~ sex + rx + adhere + differ +
## extent + node4, data = colon)
##
## coef exp(coef) se(coef) z p
## sexmale -0.03226 0.96825 0.06719 -0.480 0.63111
## rxLev -0.04495 0.95605 0.07847 -0.573 0.56681
## rxLev+5FU -0.45153 0.63665 0.08467 -5.333 9.65e-08
## adhere 0.18409 1.20212 0.09079 2.028 0.04259
## differmoderate -0.06258 0.93934 0.11625 -0.538 0.59037
## differpoor 0.27941 1.32235 0.13422 2.082 0.03737
## extentmuscle 0.21074 1.23459 0.35588 0.592 0.55374
## extentserosa 0.74471 2.10583 0.33736 2.207 0.02728
## extentcontig. 1.08395 2.95634 0.36664 2.956 0.00311
## node4 0.83820 2.31219 0.06940 12.078 < 2e-16
##
## Likelihood ratio test=246 on 10 df, p=< 2.2e-16
## n= 1812, number of events= 899
## (46 observations deleted due to missingness)
ggforest(bigmodel,
main = "Hazard ratio", # 設(shè)置標(biāo)題
cpositions = c(0.08, 0.2, 0.35), # 設(shè)置前三列的相對距離
fontsize = 0.8, # 設(shè)置字體大小
refLabel = "reference",
noDigits = 2) #設(shè)置保留小數(shù)點位數(shù)
image.png
使用forestplot包繪制森林圖
# 安裝并加載所需的R包
#install.packages("forestplot")
library(forestplot)
## Warning: package 'forestplot' was built under R version 3.6.3
## Loading required package: grid
## Loading required package: checkmate
## Warning: package 'checkmate' was built under R version 3.6.1
# 構(gòu)建示例數(shù)據(jù)
cochrane_from_rmeta <- data.frame(
mean = c(NA, NA, 0.578, 0.165, 0.246, 0.700, 0.348, 0.139, 1.017, NA, 0.531),
lower = c(NA, NA, 0.372, 0.018, 0.072, 0.333, 0.083, 0.016, 0.365, NA, 0.386),
upper = c(NA, NA, 0.898, 1.517, 0.833, 1.474, 1.455, 1.209, 2.831, NA, 0.731))
tabletext <-cbind(
c("", "Study", "Auckland", "Block",
"Doran", "Gamsu", "Morrison", "Papageorgiou",
"Tauesch", NA, "Summary"),
c("Deaths", "(steroid)", "36", "1",
"4", "14", "3", "1",
"8", NA, NA),
c("Deaths", "(placebo)", "60", "5",
"11", "20", "7", "7",
"10", NA, NA),
c("", "OR", "0.58", "0.16",
"0.25", "0.70", "0.35", "0.14",
"1.02", NA, "0.53"))
# 查看示例數(shù)據(jù)
head(cochrane_from_rmeta)
## mean lower upper
## 1 NA NA NA
## 2 NA NA NA
## 3 0.578 0.372 0.898
## 4 0.165 0.018 1.517
## 5 0.246 0.072 0.833
## 6 0.700 0.333 1.474
head(tabletext)
## [,1] [,2] [,3] [,4]
## [1,] "" "Deaths" "Deaths" ""
## [2,] "Study" "(steroid)" "(placebo)" "OR"
## [3,] "Auckland" "36" "60" "0.58"
## [4,] "Block" "1" "5" "0.16"
## [5,] "Doran" "4" "11" "0.25"
## [6,] "Gamsu" "14" "20" "0.70"
# 使用forestplot()函數(shù)繪制基礎(chǔ)森林圖
forestplot(labeltext = tabletext,
mean = cochrane_from_rmeta$mean,
lower = cochrane_from_rmeta$lower ,
upper = cochrane_from_rmeta$upper)
image.png
# 添加一些參數(shù)美化森林圖
forestplot(tabletext,
cochrane_from_rmeta,
# 添加水平線
hrzl_lines = list("1" = gpar(lty=2, lwd=2, col="black"),
"3" = gpar(lty=2, lwd=2, col="black"),
"11" = gpar(lwd=1, columns=1:4, col = "red")),
align = "c", # 設(shè)置左邊表格中字體的對齊方式
zero = 1, # 設(shè)置zero line的位置
title="Hazard Ratio Plot", # 設(shè)置標(biāo)題
new_page = TRUE,
is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE), #A vector indicating by TRUE/FALSE if the value is a summary value which means that it will have a different font-style
clip=c(0.2,2.5), #Lower and upper limits for clipping confidence intervals to arrows
xlog=TRUE,
xticks.digits = 2,
col=fpColors(box="royalblue",line="darkblue",
summary="royalblue", hrz_lines = "#444444"),
vertices = TRUE)
image.png
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Chinese (Simplified)_China.936
## [2] LC_CTYPE=Chinese (Simplified)_China.936
## [3] LC_MONETARY=Chinese (Simplified)_China.936
## [4] LC_NUMERIC=C
## [5] LC_TIME=Chinese (Simplified)_China.936
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forestplot_1.10 checkmate_1.9.4 survminer_0.4.8 ggpubr_0.2.1
## [5] magrittr_1.5 ggplot2_3.3.2 survival_2.44-1.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 pillar_1.4.2 compiler_3.6.0
## [4] tools_3.6.0 digest_0.6.20 nlme_3.1-139
## [7] evaluate_0.14 tibble_2.1.3 lifecycle_0.2.0
## [10] gtable_0.3.0 lattice_0.20-38 pkgconfig_2.0.2
## [13] rlang_0.4.7 Matrix_1.2-17 yaml_2.2.0
## [16] xfun_0.8 gridExtra_2.3 withr_2.1.2
## [19] stringr_1.4.0 dplyr_1.0.2 knitr_1.23
## [22] survMisc_0.5.5 generics_0.0.2 vctrs_0.3.2
## [25] cowplot_0.9.4 tidyselect_1.1.0 data.table_1.12.2
## [28] glue_1.4.2 KMsurv_0.1-5 R6_2.4.0
## [31] km.ci_0.5-2 rmarkdown_1.13 tidyr_1.1.2
## [34] purrr_0.3.2 backports_1.1.4 scales_1.0.0
## [37] htmltools_0.3.6 splines_3.6.0 xtable_1.8-4
## [40] colorspace_1.4-1 ggsignif_0.5.0 labeling_0.3
## [43] stringi_1.4.3 munsell_0.5.0 broom_0.5.2
## [46] crayon_1.3.4 zoo_1.8-6
