OHPL.sim {OHPL} | R Documentation |
Generate simulation data for benchmarking sparse regressions (Gaussian response)
Description
Generate simulation data (Gaussian case) following the settings in Xiao and Xu (2015).
Usage
OHPL.sim(
n = 100,
p = 100,
rho = 0.8,
coef = rep(1, 10),
snr = 3,
p.train = 0.5,
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
A list containing x.tr
, x.te
, y.tr
, and y.te
.
Author(s)
Nan Xiao <\url{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 <- OHPL.sim(
n = 100, p = 100, rho = 0.8,
coef = rep(1, 10), snr = 3, p.train = 0.5,
seed = 1010
)
dim(dat$x.tr)
dim(dat$x.te)
[Package OHPL version 1.4.1 Index]