msaenet.sim.binomial: Generate Simulation Data for Benchmarking Sparse Regressions...

In nanxstats/msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

Generate Simulation Data for Benchmarking Sparse Regressions (Binomial Response)

Description

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(s)

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$x.tr)
dim(dat$x.te)
table(dat$y.tr)
table(dat$y.te)

nanxstats/msaenet documentation built on March 8, 2024, 9:37 p.m.