Generate simulation data for benchmarking sparse regressions (Gaussian response)

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

Usage

OHPL.sim(
  n = 100,
  p = 100,
  rho = 0.8,
  coef = rep(1, 10),
  snr = 3,
  p.train = 0.5,
  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).
p.train: Percentage of training set.
seed: Random seed for reproducibility.

Value

A list containing x.tr, x.te, y.tr, and y.te.

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.

Author

Nan Xiao

Examples

dat <- OHPL.sim(
  n = 100, p = 100, rho = 0.8,
  coef = rep(1, 10), snr = 3, p.train = 0.5,
  seed = 1010
)

dim(dat$x.tr)
#> [1]  50 100
dim(dat$x.te)
#> [1]  50 100