rdasim1: Generate simulated data for Ridge Redundancy Analysis (RDA).

In rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data

Generate simulated data for Ridge Redundancy Analysis (RDA).

Description

The function generates simulated data for Ridge Redundancy Analysis (RDA). It creates two data matrices, X and Y, based on a set of shared latent variables H. The function adds noise to the data and returns a list containing the matrices X, Y, the latent variables H, and the regression coefficients theta.y used for generating Y.

Usage

rdasim1(n, p, q, k, s2n = c(5, 5))

Arguments

n	The number of samples.
p	The number of variables of X.
q	The number of variables of Y.
k	The number of latent variables.
s2n	The numeric parameters of signal to noise ratio for X and Y, default value is c(1,1).

Value

A list containing matrices X, Y, H, and theta.y.

Examples

# Example usage of rdasim1
set.seed(10)
sim_data <- rdasim1(n = 10, p = 5, q = 3, k = 2)
str(sim_data)

rrda documentation built on June 8, 2025, 12:09 p.m.