rdasim2 {rrda} | R Documentation |
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)
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. |
Value
A list containing matrices X, Y, B, E.
Examples
# Example usage of rdasim2
set.seed(10)
sim_data2 <- rdasim2(n = 10, p = 5, q = 3, k = 2)
str(sim_data2)