因果影响包：根据模型估计计算后尾区概率

Question

我目前正在使用CausalImpact包进行一些研究，在这种情况下，我需要知道并能够解释如何计算后尾区概率，以便为验证目的重现该值。有谁知道，鉴于模型提供的数据和估算系列，如何重现该值？提前致谢！

Answer 1

我从来没有使用过这个库，但是通过代码浏览，似乎他们从后验预测分布中计算出样本的分位数（alpha/2，1-alpha/2）。

来自the relevant section of code（Apache v2.0许可证）

ComputeCumulativePredictions <- function(y.samples, point.pred, y,
                                         post.period.begin, alpha = 0.05) {
  # Computes summary statistics for the cumulative posterior predictions over
  # the unobserved data points in the post-intervention period.
  #
  # Args:
  #   y.samples:         Matrix of simulated response trajectories, as returned
  #                      by \code{ComputeResponseTrajectories()}.
  #   point.pred:        Data frame of point predictions, as returned by
  #                      \code{ComputePointPredictions()}.
  #   y:                 Actual observed response, from the beginning of the
  #                      pre-period to the end of the observed period.
  #   post.period.begin: Index of the first data point of the post-period.
  #   alpha:             The resulting coverage of the posterior intervals will
  #                      be \code{1 - alpha}.
  #
  # Returns:
  #   data frame with 3 columns:
  #     cum.pred:       posterior predictive expectation
  #     cum.pred.lower: lower limit of a \code{(1 - alpha)*100}% interval
  #     cum.pred.upper: upper limit

  ... # [Computing the cum.pred.mean]

  prob.lower <- alpha / 2      # e.g., 0.025 when alpha = 0.05
  prob.upper <- 1 - alpha / 2  # e.g., 0.975 when alpha = 0.05
  cum.pred.lower.post <- as.numeric(t(apply(y.samples.cum.post, 2, quantile,
                                            prob.lower)))
  cum.pred.upper.post <- as.numeric(t(apply(y.samples.cum.post, 2, quantile,
                                            prob.upper)))
  cum.pred.lower <- c(cum.pred.lower.pre, cum.pred.lower.post)
  cum.pred.upper <- c(cum.pred.upper.pre, cum.pred.upper.post)

  # Put cumulative prediction together
  cum.pred <- data.frame(cum.pred = cum.pred.mean,
                         cum.pred.lower, cum.pred.upper)
  return(cum.pred)
}