Calibration Curve

Authors

[Editor] Hu Zheng;

[Contributors]

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: survival; rms; ggplotify

# Install packages
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(survival)
library(rms)
library(ggplotify)

Data Preparation

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

# Load data
data <- read.table("files/Hiplot/018-calibration-curve-data.txt", header = T)

# 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=0.066   Mean squared error=0.00514
0.9 Quantile of absolute error=0.098