glmtlp (version 2.0.2)

gen.gaussian.data: 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
)

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.

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.

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