Calibration Curve

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-17

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Calibration Curve plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/calibration-curve?lang=en

The calibration curve is used to evaluate the consistency / calibration, i.e. the difference between the predicted value and the real value.

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: data.table; jsonlite; survival; rms; ggplotify

# Install packages
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")
}

# Load packages
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     2026-01-17
 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.

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

Data Preparation

Data frame of multi columns data (Numeric allow NA). i.e the survival data (status with 0 and 1).

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

# convert data structure
res.lrm <- lrm(as.formula(paste(
  "status ~ ", 
  paste(colnames(data)[3:length(colnames(data))], collapse = "+"))),
  data = data, x = TRUE, y = TRUE)

lrm.cal <- calibrate(res.lrm, method = "boot", B = length(rownames(data)))

# View data
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

Visualization

# Calibration Curve
p <- as.ggplot(function() {
  plot(lrm.cal,
       xlab = "Nomogram Predicted Survival",
       ylab = "Actual Survival",
       main = "Calibration Curve"
       )
})


n=168   Mean absolute error=0.065   Mean squared error=0.00497
0.9 Quantile of absolute error=0.095