DGM {RCTrep}

Generating RCT data or observational data for the examples used in the package

Description

Generating RCT data or observational data for the examples used in the package

Usage

DGM(
  trial,
  n,
  var_name,
  p_success,
  tau,
  y0,
  log.ps = NULL,
  binary = FALSE,
  noise = 1,
  ...
)

Arguments

trial

Logical indicating whether the treatment is randomly assigned in the generated data. If TRUE, RCT data is generated. Otherwise, observational data is generated.

n

A numeric value indicating the number of observations in the generated data

var_name

A character vector indicating the names of variables

p_success

the success probability of binary variables

tau

a character indicating the generation of the true treatment effect of each individual

y0

a character indicating the generation of the potential outcome under control

log.ps

a numeric value indicating the logit of propensity score

binary

logical indicating whether the outcome is binary or continuous variable

noise

a numeric value indicating the standard error of noise term of continuous outcome

...

an optional argument indicating pairwise correlations between variables

Value

a data frame; column names are variables names, z, y

Examples

n_rct <- 500; n_rwd <- 500
var_name <- c("x1","x2","x3","x4","x5","x6")
p_success_rct <- c(0.7,0.9,0.2,0.3,0.2,0.3)
p_success_rwd <- c(0.2,0.2,0.8,0.8,0.7,0.8)
tau <- "6*x2+x6+2"
y0 <- "x1"
log.ps <- "x1*x2+x3*x4+5*x5+x6"
rho1 <- c("x1","x2",0)
rho2 <- c("x2","x3",0)

target.data <- RCTrep::DGM(trial=TRUE, n_rct, var_name,
                           p_success_rct, tau, y0, log.ps=0,
                           binary = FALSE, noise=1, rho1, rho2)
source.data <- RCTrep::DGM(trial=FALSE, n_rwd, var_name,
                           p_success_rwd, tau, y0, log.ps,
                           binary = FALSE, noise=1, rho1, rho2)