make.linear.data: Generate dataset according to a linear model.

In hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

Generate dataset according to a linear model.

Description

Given the parameters, generate a dataset and return a potential outcomes schedule (science table) of synthetic potential outcomes.

Usage

make.linear.data(
  n,
  gamma.vec = c(1, 2, 2, 1),
  gamma2.vec = NULL,
  beta.vec = c(-1, -1, 1),
  ideo.sd = 0,
  quad.tx = FALSE,
  mu.X = FALSE,
  corr.X = TRUE
)

make.quadradic.data(n, beta.vec = c(-1, -1, 1))

make.skew.data(n, beta.vec = c(-1, -1, 1))

Arguments

n	Sample size
gamma.vec	Control outcome surface
gamma2.vec	Quadratic terms
beta.vec	Treatment effect surface
ideo.sd	Ideosyncratic residual variation
quad.tx	Quadratic treatment effects?
mu.X	Center of the X covariates (can be single number or vector of length equal to the max of the length of gamma.vec, gamma2.vec, and beta.vec)
corr.X	TRUE or FALSE. Have Xs correlated or no.

Details

The control outcome surface is either linear or quadratic, of the form:

Y_i = \\gamma_0 + \\sum_{k=1}^J \\gamma_k X_{ki} + \\sum_{k=1}^{J_2} \\gamma^{(2)}_k X_{ki}^2 + \\epsilon_i

The individual treatment effects are similarly a linear or quadratic model.

Value

List of elements of data (not data frame)

Functions

• make.quadradic.data: Generate dataset according to a quadratic model

• make.skew.data: Generate dataset with a skew

hettx documentation built on Aug. 20, 2023, 1:06 a.m.