Description Usage Arguments Value
View source: R/generate_data.R
Data set contains measured covariates X, outcome Y, treatment assignment t, and logit(propensity), mu.
covariate data ~ normal(0,1); mu = true_mu; t ~ binom(p = 1 / (1 + exp(-mu))); y ~ rho * X1 + sqrt(1-rho^2) * X2 + tau * t + epsilon epsilon ~ normal(0, 1); Z instrument correllated with X1 by rho_z
1 2 3 4 5 6 7 8 9 | generate_data_IV(
N = 2000,
p = 10,
true_mu = "X1/2 + Z/2 - 3.25",
rho = 0,
rho_z = 0.2,
sigma = 1,
tau = 1
)
|
N |
numeric, sample size |
p |
numeric, number of features |
true_mu |
string formula giving true propensity score linear model |
rho |
numeric between 0 and 1. 0 => prog orthogonal to prop, 1=> prog || prop |
rho_z |
numeric between 0 and 1. correllation between Z and X1 |
sigma |
numeric noise to be added to y. y += sigma*rnorm(0,1) |
tau |
numeric additive treatment effect |
data.frame of covariates, y, t, and true prop (logit scale) and prog
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