RCS-LRM

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-01-27

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot RCS-LRM 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/rcs-lrm?lang=en

Nonlinear regression analysis.

Setup

  • System Requirements: Cross-platform (Linux/MacOS/Windows)

  • Programming language: R

  • Dependent packages: data.table; jsonlite; rms; survival; ggplot2; stringr

# Install packages
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("survival", quietly = TRUE)) {
  install.packages("survival")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("stringr", quietly = TRUE)) {
  install.packages("stringr")
}

# Load packages
library(data.table)
library(jsonlite)
library(rms)
library(survival)
library(ggplot2)
library(stringr)
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     2026-01-27
 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
 ggplot2    * 4.0.1   2025-11-14 [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
 stringr    * 1.6.0   2025-11-04 [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

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

# Convert data structure
data <- na.omit(data)
ex <- set::not(colnames(data), c("main", "group"))
ex <- str_c(ex, collapse = "+")
dd <<- datadist(data)
options(datadist = "dd")
for (i in 3:5) {
  fit <- lrm(as.formula(paste0("group~rcs(main,nk=i,inclx = T)+", ex, collapse = "+")), data = data, x = TRUE)
  tmp <- AIC(fit)
  if (i == 3) {
    AIC <- tmp
    nk <<- 3
  }
  if (tmp < AIC) {
    AIC <- tmp
    nk <<- i
  }
}
fit <- lrm(as.formula(paste0("group~rcs(main,nk=nk,inclx = T)+", ex, collapse = "+")), data = data, x = TRUE)
dd$limits$main[2] <- median(data$main)
fit <- update(fit)
orr <- Predict(fit, main, fun = exp, ref.zero = TRUE)

# View data
head(data)
  main   X2 X3 group
1  100 0.90  0     1
2   90 0.65  1     1
3  400 1.36  0     1
4  200 0.83  0     1
5  300 1.38  0     1
6  200 0.69  0     1

Visualization

# RCS-LRM
p <- ggplot() +
  geom_line(data = orr, aes(main, yhat), linetype = "solid", size = 1, alpha = 1,
            colour = "#FF0000") +
  geom_ribbon(data = orr, aes(main, ymin = lower, ymax = upper), alpha = 0.6, 
              fill = "#FFC0CB") +
  geom_hline(yintercept = 1, linetype = 2, size = 0.5) +
  geom_vline(xintercept = dd$limits$main[2], linetype = 2, size = 0.5) +
  labs(x = "main", y = "Odds Ratio(95%CI)") +
  theme_bw() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5,vjust = 1),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))
 
p
FigureΒ 1: RCS-LRM