# 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

 1 2 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

 1 2 3 4 5 6 7 8 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.