drdrtest_em.superlearner {DRDRtest}

The function for testing a effect modifier with SuperLearner

Description

This is the function for testing whether a discrete covariate is an effect modifier with SuperLearner

Usage

drdrtest_em.superlearner(
  y,
  a,
  l,
  class_label,
  arange,
  pi.sl.lib = c("SL.earth", "SL.glm", "SL.gam", "SL.glmnet"),
  mu.sl.lib = c("SL.earth", "SL.glm", "SL.gam", "SL.glmnet"),
  mu.family = "gaussian",
  h = NULL,
  b = 1000,
  dist = "TwoPoint",
  pi.low = 0.01,
  pi.var.low = 0.01,
  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

l

A data.frame containing the observations of covariates

class_label

A vector containing the class label (label for the effect modifier) for each observation.

arange

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

pi.sl.lib

Models will be used by SuperLearner to estiamte propensity scores

mu.sl.lib

Models will be used by SuperLearner to estiamte outcome regression function

mu.family

Type of response. Currently only support "gaussian" and "binomial"

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"

pi.low

Lower bound to truncate propensity scores

pi.var.low

Lower bound to truncate conditional variance of treament (used in propensity score estimation).

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

bandwidth:

Bandwidth used in kernel regression

Examples

d <- 4
n <- 200
sigma <- 0.5
delta <- 1
height <-1
arange <- c(0,5)
triangle <- function(a,height){
   y <- exp(-a^2/((1/2)^2))*height
   return(y)
}
mu.mod<-function(a,l,delta,height){
   mu <- as.numeric(l%*%c(0.2,0.2,0.3,-0.1*delta))+
         triangle(a-2.5,height)+a*(-0.1*l[,1]+0.1*delta*l[,4])
   return(mu)
}
l <- matrix(rnorm(n*d),ncol=d)
l[,4] <- ifelse(l[,4]>0,1,0)
colnames(l) <- paste("l",1:4,sep="")

logit.lambda <- as.numeric(l%*%c(0.1,0.1,-0.1,0))
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

class_label <- l[,4]
out <- drdrtest_em.superlearner(y,a,l,l[,4],arange,pi.sl.lib=c("SL.glm"),mu.sl.lib=c("SL.glm"))