# R/plot.ctseff.R In ehkennedy/npcausal: Nonparametric causal inference methods

#### Documented in plot.ctseff

#' @title Plot estimated average effect curve for continuous treatment
#'
#' @description \code{plot.ctseff} is used to plot results from \code{ctseff} fit.
#'
#' @usage plot.ctseff(ctseff.res)
#'
#' @param ctseff.res output from \code{ctseff} fit.
#'
#' @return A plot of estimated effect curve with pointwise confidence intervals.
#'
#' @examples
#' n <- 500; x <- matrix(rnorm(n*5),nrow=n)
#' a <- runif(n); y <- a + rnorm(n,sd=.5)
#'
#' ce.res <- ctseff(y,a,x, bw.seq=seq(.2,2,length.out=100))
#' plot.ctseff(ce.res)
#'
#' @references Kennedy EH, Ma Z, McHugh MD, Small DS (2017). Nonparametric methods for doubly robust estimation of continuous treatment effects. \emph{Journal of the Royal Statistical Society, Series B}. \href{https://arxiv.org/abs/1507.00747}{arxiv:1507.00747}
#'
plot.ctseff <- function(res, xlab="Treatment level A=a",ylab=expression("E( Y"^"a"~")")){

plot(res$res$a.vals, res$res$est, type="l",
ylim=c(min(res$res$ci.ll), max(res$res$ci.ul)), xlab=xlab, ylab=ylab)
lines(res$res$a.vals, res$res$ci.ll,lty=2)
lines(res$res$a.vals, res$res$ci.ul,lty=2)

}

ehkennedy/npcausal documentation built on June 20, 2018, 4:24 a.m.