ATE (version 0.2.0)

summary.ATE: Summarizing output of study.

Description

summary method for class "ATE"

Usage

"summary"(object, ...)
"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.

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.

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.

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)