诺莫图 (逻辑回归)

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 (Logistic) 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-logistic?lang=zh_cn

环境配置

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

  • 编程语言: R

  • 依赖包: data.table; jsonlite; 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("rms", quietly = TRUE)) {
  install.packages("rms")
}
if (!requireNamespace("ggplotify", quietly = TRUE)) {
  install.packages("ggplotify")
}

# 加载包
library(data.table)
library(jsonlite)
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

 [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

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

# 整理数据格式
dd <- datadist(data)
options(datadist = "dd")
## 建立 Logistic 模型并运行列线图
logistic_res <- lrm(data=data, as.formula(paste(
    colnames(data)[1], " ~ ",
    paste(colnames(data)[2:length(colnames(data))],
      collapse = "+"
    )
  ))
)
logistic_nomo <- nomogram(logistic_res, maxscale = 100,
  fun= function(x)1/(1+exp(-x)), lp=F, funlabel="Dead Risk",
  fun.at=c(.001,.01,.05,seq(.1,.9,by=.1),.95,.99,.999)
)

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

可视化

# 诺莫图 (逻辑回归)
p <- as.ggplot(function() {
  plot(logistic_nomo,
    scale = 1
  )
  title(main = "Nomogram (Logistic)")
})

p
图 1: 诺莫图 (逻辑回归)