rrda (version 0.2.3)

rdasim2: Generate simulated data for Ridge Redundancy Analysis (RDA).

Description

The rdasim2 function generates simulated data for Ridge Redundancy Analysis (RDA) with adjustable signal-to-noise ratio and covariance structure for X. The data matrix Y is created by a low-rank model, where the rank is set by the product of two matrices A and C corresponding to the number of latent variables (k). The function allows control over the signal-to-noise ratio (s2n) and off-diagonal elements of the covariance matrix for X (xofd). It returns a list containing the matrices X, Y, the regression coefficient matrix B (obtained as the product of A and C), and the error matrix E.

Usage

rdasim2(n, p, q, k, s2n = 5, xofd = 0)

Value

A list containing matrices X, Y, B, E.

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 parameter of signal to noise ratio, default value is 5.

xofd

The numeric parameter of the off-diagnal elements of covariance matrix of X, default is 0.

Examples

# Example usage of rdasim2
set.seed(10)
sim_data2 <- rdasim2(n = 10, p = 5, q = 3, k = 2)
str(sim_data2)