msaenet (version 2.1)

msaenet.sim.gaussian: 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).

p.train

Percentage of training set.

seed

Random seed for reproducibility.

Value

List of 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.

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)