Generate Simulation Data for Benchmarking Sparse Regressions (Binomial Response)

Generate simulation data for benchmarking sparse logistic regression models.

Usage

msaenet.sim.binomial(
  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.

Author

Nan Xiao <https://nanx.me>

Examples

dat <- msaenet.sim.binomial(
  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
table(dat$y.tr)
#> 
#>   0   1 
#> 101 109 
table(dat$y.te)
#> 
#>  0  1 
#> 48 42