summary.ATE: Summarizing output of study.

In ATE: Inference for Average Treatment Effects using Covariate Balancing

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.

Author(s)

Asad Haris, Gary Chan

See Also

ATE

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)

ATE documentation built on May 1, 2019, 7:33 p.m.