gen.gaussian.data: Simulate a gaussian data set

In glmtlp: Generalized Linear Models with Truncated Lasso Penalty

Simulate a gaussian data set

Description

Simulate a data set with gaussian response following the linear regression model.

Usage

gen.gaussian.data(
  n,
  p,
  rho = 0,
  kappa = 5,
  beta.type = 1,
  snr = 1,
  seed = 2021
)

Arguments

n	Sample size.
p	Number of covariates.
rho	The parameter defining the AR(1) correlation matrix.
kappa	The number of nonzero coefficients.
beta.type	Numeric indicator for choosing the beta type. For beta.type = 1, the true coefficient vector has kappa components being 1, roughly equally distributed between 1 to p. For beta.type = 2, the first kappa values are 1, and the rest are 0. For beta.type = 3, the first kappa values are equally-spaced values from 10 to 0.5, and the rest are 0. For beta.type = 4, the first kappa values are the first kappa values in c(-10, -6, -2, 2, 6, 10), and the rest are 0. For beta.type = 5, the first kappa values are 1, and the rest decay exponentially to 0 with base 0.5.
snr	Signal-to-noise ratio. Default is 1.
seed	The seed for reproducibility. Default is 2021.

Value

A list containing the simulated data.

X	the covariate matrix, of dimension n x p.
y	the response, of length n.
beta	the true coefficients, of length p.
sigma	the standard error of the noise.

Examples

gau_data <- gen.gaussian.data(n = 200, p = 20, seed = 2021)
head(gau_data$X)
head(gau_data$y)
head(gau_data$beta)
gau_data$sigma

glmtlp documentation built on March 18, 2022, 7:59 p.m.