Generate simulation data for benchmarking sparse Cox regression models.

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.

Value

List of x.tr, x.te, y.tr, and y.te.

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.

Examples

dat <- msaenet.sim.cox( n = 300, p = 500, rho = 0.6, coef = rep(1, 10), snr = 3, p.train = 0.7, seed = 1001 ) dim(dat$x.tr)
#> [1] 210 500
dim(dat$x.te)
#> [1] 90 500
dim(dat$y.tr)
#> [1] 210 2
dim(dat$y.te)
#> [1] 90 2