summary {rdlearn} | R Documentation |
Summary function
Description
This function summarizes the key results returned by rdlearn
.
Usage
summary(object, ...)
Arguments
object |
An object of class |
... |
additional arguments. |
Value
Displays key outputs from the rdlearn
function. It
provides basic information and RD causal effect estimates from
rdestimate
, as well as the safe cutoffs derived by
rdlearn
and the difference between them and the original
cutoffs.
Examples
# Simulation Data B from Appendix D of Zhang et al. (2022)
set.seed(1)
n <- 300
X <- runif(n, -1000, -1)
G <- 2 * as.numeric(
I(0.01 * X + 5 + rnorm(n, sd = 10) > 0)
) +
as.numeric(
I(0.01 * X + 5 + rnorm(n, sd = 10) <= 0)
)
c1 <- -850
c0 <- -571
C <- ifelse(G == 1, c1, c0)
D <- as.numeric(X >= C)
coef0 <- c(-1.992230e+00, -1.004582e-02, -1.203897e-05, -4.587072e-09)
coef1 <- c(9.584361e-01, 5.308251e-04, 1.103375e-06, 1.146033e-09)
Px <- poly(X, degree = 3, raw = TRUE)
# Px = poly(X-735.4334-c1,degree=3,raw=TRUE) for Simulation A
Px <- cbind(rep(1, nrow(Px)), Px)
EY0 <- Px %*% coef0
EY1 <- Px %*% coef1
d <- 0.2 + exp(0.01 * X) * (1 - G) + 0.3 * (1 - D)
Y <- EY0 * (1 - D) + EY1 * D - d * as.numeric(I(G == 1)) + rnorm(n, sd = 0.3)
simdata_B_demo <- data.frame(Y,X,C)
# Learn new treatment assignment cutoffs
rdlearn_result <- rdlearn(
y = "Y", x = "X", c = "C", data = simdata_B_demo,
fold = 2, M = 0, cost = 0
)
# Summarise the learned policies
summary(rdlearn_result)
# Visualize the learned policies
plot(rdlearn_result, opt = "dif")
# The learned cutoff for Group 1 is the same as the baseline cutoff, because
# the baseline cutoff is set to equal to oracle cutoff in this simulation.
# Implement sensitivity analysis
sens_result <- sens(rdlearn_result, M = 1, cost = 0)
plot(sens_result, opt = "dif")
[Package rdlearn version 0.1.1 Index]