R/msaenet-sim.R
msaenet.sim.gaussian.Rd
Generate simulation data (Gaussian case) following the settings in Xiao and Xu (2015).
msaenet.sim.gaussian(n = 300, p = 500, rho = 0.5, coef = rep(0.2, 50), snr = 1, p.train = 0.7, seed = 1001)
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. |
List of x.tr
, x.te
, y.tr
, and y.te
.
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.
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)#> [1] 210 500#> [1] 90 500