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#' Cutoff Sensitivity Simulation for Regression Discontinuity
#'
#' \code{rd_sens_cutoff} refits the supplied model with varying cutoff(s).
#' All other aspects of the model, such as the automatically calculated bandwidth, are held constant.
#'
#' @param object An object returned by \code{rd_est} or \code{rd_impute}.
#' @param cutoffs A numeric vector of cutoff values to be used for refitting
#' an \code{rd} object.
#'
#' @return \code{rd_sens_cutoff} returns a dataframe containing the estimate \code{est} and standard error \code{se}
#' for each cutoff value (\code{A1}). Column \code{A1} contains varying cutoffs
#' on the assignment variable. The \code{model} column contains the parametric model (linear, quadratic, or cubic) or
#' non-parametric bandwidth setting (Imbens-Kalyanaraman 2012 optimal, half, or double) used for estimation.
#'
#' @references Imbens, G., Kalyanaraman, K. (2012).
#' Optimal bandwidth choice for the regression discontinuity estimator.
#' The Review of Economic Studies, 79(3), 933-959.
#' \url{https://academic.oup.com/restud/article/79/3/933/1533189}.
#'
#' @export
#'
#' @examples
#' set.seed(12345)
#' x <- runif(1000, -1, 1)
#' cov <- rnorm(1000)
#' y <- 3 + 2 * x + 3 * cov + 10 * (x >= 0) + rnorm(1000)
#' rd <- rd_est(y ~ x | cov, t.design = "geq")
#' rd_sens_cutoff(rd, seq(-.5, .5, length.out = 10))
rd_sens_cutoff <- function(object, cutoffs) {
if (!inherits(object, "rd"))
stop("Not an object of class rd.")
sim_results <- lapply(cutoffs,
function(cutoff) {
object$call$cutpoint <- cutoff
object$call$est.cov <- FALSE
object$call$bw <- object$bw["Opt"]
new_model <- eval.parent(object$call, 3)
return(
data.frame(
est = new_model$est,
se = new_model$se,
A1 = cutoff,
model = c("linear", "quadratic", "cubic", "optimal", "half", "double"),
stringsAsFactors = FALSE,
row.names = 1:6)
)
}
)
combined_sim_results <- do.call(rbind.data.frame, sim_results)
original_results <- data.frame(
est = object$est,
se = object$se,
A1 = if (is.null(object$call$cutpoint)) 0 else eval.parent(object$call$cutpoint),
model = c("linear", "quadratic", "cubic", "optimal", "half", "double"),
stringsAsFactors = FALSE,
row.names = 1:6)
return(rbind(combined_sim_results, original_results))
}
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