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

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

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 β)}{Var(\varepsilon)} = \frac{Var(β^T Σ β)}{σ^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)

Example output

[1] 210 500
[1]  90 500

msaenet documentation built on May 18, 2019, 1:03 a.m.