simulXy: Simulate Model Matrix and Response Vector

In islasso: The Induced Smoothed Lasso

Simulate Model Matrix and Response Vector

Description

Generates synthetic covariates and response vector from a specified distribution for simulation studies or method validation.

Usage

simulXy(
  n,
  p,
  interc = 0,
  beta,
  family = gaussian(),
  prop = 0.1,
  lim.b = c(-3, 3),
  sigma = 1,
  size = 1,
  rho = 0,
  scale.data = TRUE,
  seed = NULL,
  X = NULL,
  dispersion = 0.1
)

Arguments

n	Integer. Number of observations.
p	Integer. Total number of covariates in the model matrix.
interc	Numeric. Intercept to include in the linear predictor. Default is 0.
beta	Numeric vector of length p. Regression coefficients in the linear predictor.
family	Distribution and link function. Allowed: gaussian(), binomial(), poisson() and , Gamma(). Can be a string, function, or family object.
prop	Numeric in [0,1]. Used only if beta is missing; proportion of non-zero coefficients in p. Default is 0.1.
lim.b	Numeric vector of length 2. Range for coefficients if beta is missing. Default: c(-3, 3).
sigma	Standard deviation of Gaussian response. Default is 1.
size	Integer. Number of trials for binomial response. Default is 1.
rho	Numeric. Correlation coefficient for generating covariates. Used to create AR(1)-type covariance: rho^|i-j|. Default is 0.
scale.data	Logical. Whether to scale columns of the model matrix. Default is TRUE.
seed	Optional. Integer seed for reproducibility.
X	Optional. Custom model matrix. If supplied, it overrides the internally generated X.
dispersion	Dispersion parameter of Gamma response. Default is 0.1.

Value

A list with components:

X	Model matrix of dimension n x p
y	Simulated response vector
beta	True regression coefficients used
eta	Linear predictor

Examples

n <- 100; p <- 100
beta <- c(runif(10, -3, 3), rep(0, p - 10))
sim <- simulXy(n = n, p = p, beta = beta, seed = 1234)
o <- islasso(y ~ ., data = sim$data, family = gaussian())
summary(o, pval = 0.05)

islasso documentation built on Aug. 9, 2025, 1:06 a.m.