msaenet.sim.gaussian: Generate Simulation Data for Benchmarking Sparse Regressions...

In nanxstats/msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

Generate Simulation Data for Benchmarking Sparse Regressions (Gaussian Response)

Description

Generate simulation data (Gaussian case) following the settings in Xiao and Xu (2015).

Usage

msaenet.sim.gaussian(
  n = 300,
  p = 500,
  rho = 0.5,
  coef = rep(0.2, 50),
  snr = 1,
  p.train = 0.7,
  seed = 1001
)

Arguments

n	Number of observations.
p	Number of variables.
rho	Correlation base for generating correlated variables.
coef	Vector of non-zero coefficients.
snr	Signal-to-noise ratio (SNR). SNR is defined as \frac{Var(E(y | X))}{Var(Y - E(y | X))} = \frac{Var(f(X))}{Var(\varepsilon)} = \frac{Var(X^T \beta)}{Var(\varepsilon)} = \frac{Var(\beta^T \Sigma \beta)}{\sigma^2}.
p.train	Percentage of training set.
seed	Random seed for reproducibility.

Value

List of x.tr, x.te, y.tr, and y.te.

Author(s)

Nan Xiao <https://nanx.me>

References

Nan Xiao and Qing-Song Xu. (2015). Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection. Journal of Statistical Computation and Simulation 85(18), 3755–3765.

Examples

dat <- msaenet.sim.gaussian(
  n = 300, p = 500, rho = 0.6,
  coef = rep(1, 10), snr = 3, p.train = 0.7,
  seed = 1001
)

dim(dat$x.tr)
dim(dat$x.te)

nanxstats/msaenet documentation built on July 24, 2024, 12:47 p.m.