OHPL.sim: Generate simulation data for benchmarking sparse regressions...

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Generate simulation data for benchmarking sparse regressions (Gaussian response)

Description

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.

Author(s)

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

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

OHPL documentation built on Sept. 11, 2024, 7:05 p.m.