BinomialModel: Binomial Model Construction

In IOBR: Immune Oncology Biological Research

Binomial Model Construction

Description

Constructs and evaluates binomial logistic regression models using Lasso and Ridge regularization. Processes input data, scales features if specified, splits data into training/testing sets, and fits both Lasso and Ridge models. Optionally generates AUC plots for model evaluation.

Usage

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

Arguments

x	A data frame containing sample ID and features. First column must be sample ID.
y	A data frame where first column is sample ID and second column is outcome (numeric or factor).
seed	Integer for random seed. Default is '123456'.
scale	Logical indicating whether to scale features. Default is 'TRUE'.
train_ratio	Numeric between 0 and 1 for training proportion. Default is '0.7'.
nfold	Integer for cross-validation folds. Default is '10'.
plot	Logical indicating whether to generate AUC plots. Default is 'TRUE'.
palette	Character string for color palette. Default is '"jama"'.
cols	Optional color vector for ROC curves. Default is 'NULL'.

Value

List containing:

lasso_result

Lasso model results

ridge_result

Ridge model results

train.x

Training data with IDs

Author(s)

Dongqiang Zeng

Examples

set.seed(123)
x <- data.frame(
  ID = paste0("Sample", 1:50),
  Feature1 = rnorm(50),
  Feature2 = rnorm(50),
  Feature3 = rnorm(50)
)
y <- data.frame(
  ID = x$ID,
  Outcome = factor(rbinom(50, 1, 0.5))
)
result <- BinomialModel(x = x, y = y, plot = FALSE, nfold = 5)
str(result, max.level = 1)

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