OHPL.sim: Generate Simulation Data for Benchmarking Sparse Regressions...

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

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

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 <- 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 May 18, 2019, 9:03 a.m.