genDataGrp: Simulate grouped data for regression models

In pbreheny/hdrm: High-Dimensional Regression Modeling

Simulate grouped data for regression models

Description

Simulate grouped data for regression models

Usage

genDataGrp(
  n,
  J,
  K = 1,
  beta,
  family = c("gaussian", "binomial"),
  J1 = ceiling(J/2),
  K1 = K,
  SNR = 1,
  signal = c("homogeneous", "heterogeneous"),
  signal.g = c("homogeneous", "heterogeneous"),
  rho = 0,
  rho.g = rho
)

Arguments

n	Sample size
J	Number of groups
K	Number of features per group
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
J1	Number of nonzero groups
K1	Number of nonzero coefficients per group
SNR	Signal to noise ratio
signal	Should the groups be heterogeneous (in beta) or homogeneous?
signal.g	Should the coefficients within a group be heterogeneous or homogeneous?
rho	Correlation between groups
rho.g	Correlation between parameters within a group

Examples

Data <- genDataGrp(100, 10, 5, J1=3, K1=2)
dim(Data$X)
head(Data$y)
Data$beta
Data$group

genDataGrp(100, 3, 3, J1=2, K1=2)$beta
genDataGrp(100, 3, 3, J1=2, K1=2, SNR=2)$beta
genDataGrp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8)$beta
genDataGrp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het')$beta
genDataGrp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het', signal.g='het')$beta
genDataGrp(100, 3, 3, J1=2, K1=2, SNR=2, rho=0.8, signal='het', b=1)$beta

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