msaenet.sim.poisson {msaenet}

Generate Simulation Data for Benchmarking Sparse Regressions (Poisson Response)

Description

Generate simulation data for benchmarking sparse Poisson regression models.

Usage

msaenet.sim.poisson(
  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.poisson(
  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)