genDataABN: Simulate data according to Causal/Correlated/Noise paradigm

In pbreheny/hdrm: High-Dimensional Regression Modeling

Simulate data according to Causal/Correlated/Noise paradigm

Description

Simulate data according to Causal/Correlated/Noise paradigm

Usage

genDataABN(
  n = 100,
  p = 60,
  a = 6,
  b = 2,
  rho = 0.5,
  family = c("gaussian", "binomial"),
  signal = c("homogeneous", "heterogeneous"),
  noise = c("exchangeable", "autoregressive"),
  rho.noise = 0,
  beta,
  SNR = 1
)

Arguments

n	Sample size
p	Number of features
a	Number of causal ('A') variables
b	Number of correlated ('B') variables per causal ('A') variable
rho	Correlation between 'A' and 'B' variables
family	Generate y according to linear "gaussian" or logistic "binomial" model
signal	Should the groups be heterogeneous (in beta) or homogeneous?
noise	Correlation structure between features ('exchangeable' | 'autoregressive')
rho.noise	Correlation parameter for noise variables
beta	Vector of regression coefficients in the generating model. Should be either a scalar, in which case it represents the value of each nonzero regression coefficient, or a vector, in which case it should be of length a
SNR	Signal to noise ratio

Examples

Data <- genDataABN(n=100, p=20, a=2, b=3)
dim(Data$X)
length(Data$y)
with(Data, data.frame(beta, varType))

genDataABN(100, 10, 2, 1)$beta
genDataABN(100, 10, 2, 1, rho=0.9)$beta
genDataABN(100, 10, 2, 1, rho=0.9, rho.noise=0.9)$beta
genDataABN(100, 10, 2, 1, SNR=3)$beta
genDataABN(100, 10, 2, 1, SNR=3, signal='het')$beta
genDataABN(100, 10, 2, 1, beta=3)$beta
genDataABN(100, 10, 2, 1, beta=10:1)$beta

genDataABN(10, 20, 2, 3, family='binomial')$y

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