genData: Simulate data for regression models

In pbreheny/hdrm: High-Dimensional Regression Modeling

Simulate data for regression models

Description

Simulate data for regression models

Usage

genData(
  n,
  p,
  p1 = floor(p/2),
  beta,
  family = c("gaussian", "binomial"),
  SNR = 1,
  signal = c("homogeneous", "heterogeneous"),
  corr = c("exchangeable", "autoregressive"),
  rho = 0
)

Arguments

n	Sample size
p	Number of features
p1	Number of nonzero features
beta	Vector of regression coefficients in the generating model, or, if a scalar, the value of each nonzero regression coefficient.
family	Generate y according to linear "gaussian" or logistic "binomial" model
SNR	Signal to noise ratio
signal	Should the beta coefficients be homogeneous (default) or heterogeneous
corr	Correlation structure between features ('exchangeable' | 'autoregressive')
rho	Correlation coefficient

Examples

Data <- genData(100, 100, 10)
dim(Data$X)
head(Data$y)
head(Data$beta)

genData(100, 10, 5)$beta
genData(100, 10, 5, SNR=2)$beta
genData(100, 10, 5, SNR=2, corr='exch', rho=0.7)$beta
genData(100, 10, 5, SNR=2, corr='auto', rho=0.7)$beta
genData(100, 10, 5, SNR=2, corr='auto', rho=0.7, signal='het')$beta
genData(100, 10, 5, SNR=2, corr='auto', rho=0.1, signal='het')$beta
genData(100, 10, 5, SNR=2, corr='auto', rho=0.1, signal='het', b=1)$beta

genData(10, 10, 5, family='binomial')$y

pbreheny/hdrm documentation built on Jan. 17, 2024, 8:53 p.m.