Simulate a sparse quantile-regression problem

Usage

simulate_quantile_data(
  n = 200,
  p = 40,
  active = 1:5,
  beta = c(2, 1.5, -1.5, 1, -1),
  tau = 0.5,
  rho = 0.7,
  correlation = c("toeplitz", "block"),
  block_size = 5L,
  error = c("gaussian", "student", "laplace", "heteroskedastic"),
  error_df = 3,
  heteroskedastic_strength = 0.75,
  seed = NULL
)

Arguments

n: Number of observations.
p: Number of predictors.
active: Indices of active predictors.
beta: Coefficients for the active predictors. Recycled as needed.
tau: Quantile level whose conditional linear predictor is controlled.
rho: Toeplitz correlation parameter for the predictors.
correlation: Correlation structure. One of "toeplitz" or "block".
block_size: Block size used when correlation = "block".
error: Error distribution. One of "gaussian", "student", "laplace", or "heteroskedastic".
error_df: Degrees of freedom when error = "student".
heteroskedastic_strength: Positive scale multiplier used when error = "heteroskedastic".
seed: Optional random seed.

Value

A list containing x, y, beta, active, tau, and the simulation settings used to generate the data.

Examples

sim <- simulate_quantile_data(seed = 42)
str(sim, max.level = 1)
#> List of 9
#>  $ x          : num [1:200, 1:40] 0.906 -0.457 0.911 1.373 -0.415 ...
#>   ..- attr(*, "dimnames")=List of 2
#>  $ y          : num [1:200] -1.356 -0.868 1.324 5.481 -1.908 ...
#>  $ beta       : num [1:40] 2 1.5 -1.5 1 -1 0 0 0 0 0 ...
#>  $ active     : chr [1:5] "x1" "x2" "x3" "x4" ...
#>  $ tau        : num 0.5
#>  $ rho        : num 0.7
#>  $ correlation: chr "toeplitz"
#>  $ block_size : int 5
#>  $ error      : chr "gaussian"