msaenet.sim.gaussian | R Documentation |
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). SNR is defined as
|
p.train |
Percentage of training set. |
seed |
Random seed for reproducibility. |
List of x.tr
, x.te
, y.tr
, and y.te
.
Nan Xiao <https://nanx.me>
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$x.tr)
dim(dat$x.te)
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