
Generate Simulation Data for Benchmarking Sparse Regressions (Cox Model)
Source:R/msaenet-sim.R
msaenet.sim.cox.Rd
Generate simulation data for benchmarking sparse Cox regression models.
Usage
msaenet.sim.cox(
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).
- p.train
Percentage of training set.
- seed
Random seed for reproducibility.
References
Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2011). Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software, 39(5), 1--13.
Author
Nan Xiao <https://nanx.me>