est_fncn: Estimation function - Double Sample Approach

Description Usage Arguments Examples

View source: R/est_fncn.R

Description

This function estimates the treatment effect using a known tree structure

Usage

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est_fncn(Y, Z, X, X.i, grp.ids, LT)

Arguments

Y

response vector

Z

treatment indicator

X

covariate matrix

X.i

covariate matrix

grp.ids

IDs of groups

LT

Tree list - double sample

Examples

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N<-2000
numx <- 5
alpha <-0.8
theta<-0.8
beta<- c(1,.8,.6,.4,.2)
gamma <- 1
Z <- rep(c(0, 1), each = N/2)
sigma <- diag(numx)
X.i <- mvrnorm(N,mu=rep(0,numx),Sigma=sigma)
W <- Z * ifelse(X.i[,1] > 0, 1, 0)
mu <- alpha + theta*Z + X.i %*% beta + W * gamma
Y <- rnorm(N, mean=mu)

Nused <- 200
subjects <- c(1:(Nused/2), 1001:(1000+Nused/2))
#Using a double sample approach
res <- LTfunction(Y[subjects], Z[subjects], X.i[subjects,], X.i[subjects,], sample= "double")
n1<-(Nused/4)+1
n2<-(Nused/2)
n3<-(1000+(Nused/4))+1
n4<-(1000+(Nused/2))
test.subj <- c(n1:n2,n3:n4)
# Case when a treated patient has characteristics defined by X1 > 0
test.case <- which(X.i[test.subj,1] > 0 & Z[test.subj] == 1)[1]
# Get the ids in the leaf that the test case belongs to
group.ids <- grp_func(res, test.case)
trt.effect <- est_fncn(Y[test.subj], Z[test.subj], X.i[test.subj,], X.i[test.subj,], group.ids, res)

AshwiniKV/TEHTree documentation built on Sept. 15, 2021, 11:21 p.m.