data_gen: Normal regression data generator

In SCPME: Shrinking Characteristics of Precision Matrix Estimators

Description

True beta values are generated from p*r independent draws from N(0, 1/p) distribution. X are n independent draws from p multivariate normal N(0, SigmaX). Y is then generated using X and true beta values with an iid error term that follows r multivariate normal distribution N(0, Sigma).

Usage

data_gen(n, p, r = 1, sparsity = 0.5, Sigma = c("tridiag", "dense",
  "denseQR", "compound"), s = NULL, SigmaX = c("tridiag", "dense",
  "denseQR", "compound"), sx = NULL, ...)

Arguments

n	desired sample size
p	desired dimension
r	number of responses
sparsity	desired sparsity for beta
Sigma	covariance matrix structure used to generate Y | X
s	option to specify diagonal elements in Sigma
SigmaX	covariance matrix structure used to generate data X
sx	option to specify diagonal elements in SigmaX
...	additional arguments to pass to data generating functions

Value

Y, X, betas, Sigma, SigmaX

Author(s)

Matt Galloway [e-mail]

Examples

# generate 100 observations with predictor dimension equal to 10 and response dimension equal to 5
data = data_gen(n = 100, p = 10, r = 5)

SCPME documentation built on May 2, 2019, 3:42 a.m.