drdrtest.base {DRDRtest}

The base function for performing tests of average treatment effects

Description

This is the base function for testing average treatment effects. Users can use specify the nuisance function values by themselves.

Usage

drdrtest.base(
  y,
  a,
  pi,
  varpi,
  mu,
  ma,
  arange,
  h = NULL,
  b = 1000,
  dist = "TwoPoint",
  a.grid.size = 401
)

Arguments

y

A vector containing the outcomes for each observation

a

A vector containing the treatment levels (dosage) for each observation

pi

A vector containing the propensity scores for each observation

varpi

A vector containing the mean propensity scores for each observation

mu

A vector containing the outcome regression function values for each observation

ma

A vector containing the mean outcome regression fucntion values for each observation

arange

A vector of length 2 giving the lower bound and upper bound of treatment levels

h

bandwidth to be used in kernel regression. If not specified, will by default use "rule of thumb" bandwidth selector

b

number of Bootstrap samples to be generated

dist

distibution used to generate residuals for Bootstrap samples. Currently only have two options, "TwoPoint" and "Rademachar"

a.grid.size

size of equally spaced grid points over arange to be generate for numerically evaluating the integral in test statistic

Value

A list containing

p.value:

P value of the test result

test.stat:

Value of the observed test statistic

Bootstrap.samples:

A vector containing test statistic values from Bootstrap samples

loc.fit:

A list containg evalution points of average treatment effect and the corresponding values

bandwidth:

Bandwidth used in kernel regression

Examples

mu.mod<-function(a,l,delta,height){
    mu <- as.numeric(l%*%c(0.2,0.2,0.3,-0.1))+triangle(a-2.5,delta,height)+a*(-0.1*l[,1]+0.1*l[,3])
    return(mu)
}
triangle <- function(a,delta,height){
    y <- exp(-a^2/((delta/2)^2))*height
    return(y)
}
set.seed(2000)
n <- 500
d <- 4
sigma <- 0.5
delta <- 1
height <- 0
arange<-c(0.01,4.99)

l <- matrix(rnorm(n*d),ncol=d)
colnames(l) <- paste("l",1:4,sep="")
logit.lambda <- as.numeric(l%*%c(0.1,0.1,-0.1,0.2))
lambda <- exp(logit.lambda)/(1+exp(logit.lambda))
a <- rbeta(n, shape1 = lambda, shape2 =1-lambda)*5

mu <- mu.mod(a,l,delta,height)
residual.list <- rnorm(n,mean=0,sd=sigma)
y <- mu+residual.list

## We use the oracal propensity score and outcome regression for illustration
pilist <- dbeta(a/5, shape1=lambda, shape2 = 1-lambda)/5
varpilist <- colMeans(matrix(dbeta(rep(a,each=n)/5,
                                   shape1=rep(lambda,n),
                                   shape2 = 1-rep(lambda,n))/5, nrow=n))
mulist <- mu
malist <-colMeans(matrix(mu.mod(rep(a,each=n),l[rep(1:n,n),],delta,height),nrow=n))

out <- drdrtest.base(y,a,pilist,varpilist,mulist,malist,arange)