PrognosticModel: Build Prognostic Models Using LASSO and Ridge Regression

In IOBR: Immune Oncology Biological Research

Build Prognostic Models Using LASSO and Ridge Regression

Description

Prepares data, splits it into training and testing sets, and fits LASSO and Ridge regression models for survival analysis. Evaluates model performance using cross-validation and optionally generates time-dependent ROC curves for visual assessment of predictive accuracy.

Usage

PrognosticModel(
  x,
  y,
  scale = FALSE,
  seed = 123456,
  train_ratio = 0.7,
  nfold = 10,
  plot = TRUE,
  palette = "jama",
  cols = NULL
)

Arguments

x	A matrix or data frame of predictor variables (features).
y	A data frame of survival outcomes with two columns: survival time and event status.
scale	Logical indicating whether to scale predictor variables. Default is 'FALSE'.
seed	Integer seed for random number generation to ensure reproducibility. Default is '123456'.
train_ratio	Numeric proportion of data for training (e.g., 0.7). Default is '0.7'.
nfold	Integer number of folds for cross-validation. Default is '10'.
plot	Logical indicating whether to plot ROC curves. Default is 'TRUE'.
palette	String specifying color palette. Default is '"jama"'.
cols	Optional vector of colors for ROC curves. If 'NULL', uses default palette.

Value

A list containing:

lasso_result

Results from LASSO model including coefficients and AUC

ridge_result

Results from Ridge model including coefficients and AUC

train.x

Training data with sample IDs

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  # Create small example data (first column must be ID)
  x_sim <- as.data.frame(matrix(rnorm(100 * 5), 100, 5))
  colnames(x_sim) <- paste0("Sig", 1:5)
  x_sim$ID <- paste0("S", 1:100)
  x_sim <- x_sim[, c(6, 1:5)] # Move ID to first column

  y_sim <- data.frame(
    ID = paste0("S", 1:100),
    OS_days = rexp(100, 0.01),
    OS_status = rbinom(100, 1, 0.5)
  )
  prognostic_result <- PrognosticModel(
    x = x_sim, y = y_sim,
    scale = TRUE, seed = 123456,
    train_ratio = 0.7, nfold = 3, plot = FALSE
  )
  if (!is.null(prognostic_result)) head(prognostic_result)
}

IOBR documentation built on May 30, 2026, 5:07 p.m.