`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