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`.

`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)

```

[Package ATE version 0.2.0 Index]