getTrainTest: getTrainTest

In Coxmos: Cox MultiBlock Survival

getTrainTest

Description

Splits input data (X and Y) into training and test sets for survival analysis, ensuring balanced event distributions. Supports single or multiple splits (repeats) for cross-validation and multiblock data in X parameter.

Usage

getTrainTest(X, Y, p = 0.8, times = 1, seed = 123)

Arguments

X	Numeric matrix, data.frame or list of matrices or data.frames. Predictor variables (features). Rows are samples, columns are variables.
Y	Numeric matrix or data.frame. Response variables. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.
p	Numeric (0 < p < 1). Proportion of samples to allocate to the training set (default: 0.8).
times	Integer. Number of splits to perform repeated partitioning (default: 1).
seed	Integer. Random seed for reproducibility (default: 123).

Details

This function uses caret::createDataPartition() to partition the data while preserving the proportion of events (e.g., deaths) in both training and test sets. It is designed for survival data where Y must contain an event column (binary: 1=event, 0=censored).

Value

• If times = 1: A list with:

• X_train: Training features.

• Y_train: Training survival data.

• X_test: Test features.

• Y_test: Test survival data.

• If times > 1: A named list of length times, each element containing the above structure.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

See Also

createDataPartition

Examples

# Single split (80% training, 20% test)
data(X_proteomic, Y_proteomic)
lst <- getTrainTest(X_proteomic, Y_proteomic, p = 0.8)

# Repeated splits (3x)
lst_repeats <- getTrainTest(X_proteomic, Y_proteomic, p = 0.7, times = 3)

Coxmos documentation built on June 8, 2025, 10:30 a.m.