summary.ATE {ATE} R Documentation

Summarizing output of study.

Description

summary method for class "ATE"

Usage

## S3 method for class 'ATE'
summary(object, ...)

## S3 method for class 'summary.ATE'
print(x, ...)

Arguments

 object An object of class "ATE", usually a result of a call to ATE. x An object of class "summary.ATE", usually a result of a call to summary.ATE. ... Further arguments passed to or from methods.

Details

print.summary.ATE prints a simplified output similar to print.summary.lm. The resulting table provides the point estimates, estimated standard errors, 95% Wald confidence intervals, the Z-statistic and the P-values for a Z-test.

Value

The function summary.ATE returns a list with the following components

 Estimate A matrix with point estimates along with standard errors, confidence intervals etc. This is the matrix users see with the print.summary.RIPW function. vcov The variance-covariance matrix of the point estimates. Conv The convergence result of the object. weights The weights for each subject in each treatment arm. These are same as the weight component of the "RIPW" object. call The call passed on as an argument of the function which is equivalent to object\$call.

Examples

library(ATE)
#binary treatment

set.seed(25)
n <- 200
Z <- matrix(rnorm(4*n),ncol=4,nrow=n)
prop <- 1 / (1 + exp(Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4]))
treat <- rbinom(n, 1, prop)
Y <- 200 + 10*treat+ (1.5*treat-0.5)*(27.4*Z[,1] + 13.7*Z[,2] +
13.7*Z[,3] + 13.7*Z[,4]) + rnorm(n)
X <- cbind(exp(Z[,1])/2,Z[,2]/(1+exp(Z[,1])),
(Z[,1]*Z[,3]/25+0.6)^3,(Z[,2]+Z[,4]+20)^2)

#estimation of average treatment effects (ATE)
fit1<-ATE(Y,treat,X)
summary(fit1)
#plot(fit1)

#estimation of average treatment effects on treated (ATT)
fit2<-ATE(Y,treat,X,ATT=TRUE)
summary(fit2)
#plot(fit2)

#three treatment groups
set.seed(25)
n <- 200
Z <- matrix(rnorm(4*n),ncol=4,nrow=n)
prop1 <- 1 / (1 + exp(1+Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4]))
prop2 <- 1 / (1 + exp(Z[,1] - 0.5 * Z[,2] + 0.25*Z[,3] + 0.1 * Z[,4]))

U <-runif(n)
treat <- numeric(n)
treat[U>(1-prop2)]=2
treat[U<(1-prop2)& U>(prop2-prop1)]=1

Y <- 210 + 10*treat +(27.4*Z[,1] + 13.7*Z[,2] +
13.7*Z[,3] + 13.7*Z[,4]) + rnorm(n)
X <- cbind(exp(Z[,1])/2,Z[,2]/(1+exp(Z[,1])),
(Z[,1]*Z[,3]/25+0.6)^3,(Z[,2]+Z[,4]+20)^2)

fit3<-ATE(Y,treat,X)
summary(fit3)
#plot(fit3)

[Package ATE version 0.2.0 Index]