Generate simulation data for benchmarking sparse Poisson regression models.

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. Number of variables. Correlation base for generating correlated variables. Vector of non-zero coefficients. Signal-to-noise ratio (SNR). Percentage of training set. Random seed for reproducibility.

## Value

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

## 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$x.tr)#> [1] 210 500dim(dat$x.te)#> [1]  90 500