诺莫图

作者

[编辑] 郑虎;

[审核] .

修改于

2026-01-27

注记

Hiplot 网站

本页面为 Hiplot Nomogram 插件的源码版本教程，您也可以使用 Hiplot 网站实现无代码绘图，更多信息请查看以下链接:

https://hiplot.cn/basic/nomogram?lang=zh_cn

诺莫图常被用来评价肿瘤和医学的预后，并能直观地反映logistic回归或Cox回归的结果。

环境配置

  • 系统: Cross-platform (Linux/MacOS/Windows)

  • 编程语言: R

  • 依赖包: data.table; jsonlite; survival; rms; ggplotify

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("survival", quietly = TRUE)) {
  install.packages("survival")
}
if (!requireNamespace("rms", quietly = TRUE)) {
  install.packages("rms")
}
if (!requireNamespace("ggplotify", quietly = TRUE)) {
  install.packages("ggplotify")
}

# 加载包
library(data.table)
library(jsonlite)
library(survival)
library(rms)
library(ggplotify)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-01-28
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package    * version date (UTC) lib source
 data.table * 1.18.0  2025-12-24 [1] RSPM
 ggplotify  * 0.1.3   2025-09-20 [1] RSPM
 Hmisc      * 5.2-5   2026-01-09 [1] RSPM
 jsonlite   * 2.0.0   2025-03-27 [1] RSPM
 rms        * 8.1-0   2025-10-14 [1] RSPM
 survival   * 3.8-3   2024-12-17 [3] CRAN (R 4.5.2)

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

数据准备

随时间变化的生存数据帧，根据实例数据用0,1等数字表示性别和状态。

# 加载数据
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/nomogram/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# 整理数据格式
dd <- datadist(data)
options(datadist = "dd")
## 建立 COX 模型并运行列线图
cox_res <- psm(
  data = data,
  as.formula(paste(
    sprintf("Surv(%s, %s) ~ ", colnames(data)[1], colnames(data)[2]),
    paste(colnames(data)[3:length(colnames(data))],
      collapse = "+"
    )
  )),
  # Surv(time, status) ~ age + sex + ph.ecog + ph.karno + pat.karno,
  dist = "lognormal"
)
## 建立 Survival 概率函数
surv <- Survival(cox_res)
## 构建分位数生存时间函数
med <- Quantile(cox_res)

cox_nomo <- nomogram(
  cox_res,
  fun = list(function(x) surv(365, x), function(x) surv(1095, x),
             function(x) surv(1825, x), function(x) med(lp = x)),
  funlabel = c("1-year Survival Probability",
               "3-year Survival Probability",
               "5-year Survival Probability",
               "Median Survival Time"),
  maxscale = 100
)

# 查看数据
head(data)
  time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
1  306      2  74   1       1       90       100     1175      NA
2  455      2  68   1       0       90        90     1225      15
3 1010      1  56   1       0       90        90       NA      15
4  210      2  57   1       1       90        60     1150      11
5  883      2  60   1       0      100        90       NA       0
6 1022      1  74   1       1       50        80      513       0

可视化

# 诺莫图
p <- as.ggplot(function() {
  plot(cox_nomo, scale = 1)
  title(main = "Nomogram (COX)")
})

p
图 1: 诺莫图