Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/main_function.R
summary
method for class 'ATE'
.
1 2 3 4 5 |
object |
An object of class |
x |
An object of class |
... |
Further arguments passed to or from methods. |
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.
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 |
vcov |
The variance-covariance matrix of the point estimates. |
Conv |
The convergence result of the |
weights |
The weights for each subject in each treatment arm. These are same as the |
call |
The |
Asad Haris, Gary Chan
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | 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)
# Four 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)
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