R/sample_posterior_R.R

In annecori/EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves

#' sample from the posterior R distribution
#'
#' @param R an \code{estimate_R} object from the estimate_r function
#'   function.
#' 
#' @param n an integer specifying the number of samples to be taken from the
#'   gamma distribution.
#' 
#' @param window an integer (or sequence of integers) specifying the window(s) 
#'   from which to estimate R. Defaults to the first window. If multiple windows
#'   are specified, the resulting samples will be drawn from several 
#'   distributions.
#' 
#' @return n values of R from the posterior R distribution
#'
#' @author Anne Cori
#' @export
#'
#' @examples
#'
#'
#' ## load data on pandemic flu in a school in 2009
#' data("Flu2009")
#'
#' ## estimate the reproduction number (method "non_parametric_si")
#' ## when not specifying t_start and t_end in config, they are set to estimate
#' ## the reproduction number on sliding weekly windows                          
#' res <- estimate_R(incid = Flu2009$incidence, 
#'                   method = "non_parametric_si",
#'                   config = make_config(list(si_distr = Flu2009$si_distr)))
#' 
#' ## Sample R from the first weekly window
#' win <- 1L
#' R_median <- res$R$`Median(R)`[win]
#' R_CrI <- c(res$R$`Quantile.0.025(R)`[win], res$R$`Quantile.0.975(R)`[win])
#'
#' set.seed(2019-06-06) # fixing the random seed for reproducibility
#' R_sample <- sample_posterior_R(res, n = 1000, window = win)
#' hist(R_sample, col = "grey", main = "R sampled from the first weekly window")
#' abline(v = R_median, col = "red")       # show the median estimated R
#' abline(v = R_CrI, col = "red", lty = 2) # show the 95%CrI of R
sample_posterior_R <- function(R, n = 1000, window = 1L) {

  if (!inherits(R, "estimate_R")) {
    stop("R must be generated from the estimate_r function.")
  }
  mu    <- R$R$`Mean(R)`[window]
  sigma <- R$R$`Std(R)`[window]
  # Gamma distribution needs a shape and scale, which are transformations
  # of the mean (mu) and standard deviation (sigma) 
  cv    <- sigma / mu # coefficient of variation
  shape <- 1 / (cv ^ 2)
  scale <- mu * cv ^ 2
  rgamma(n, shape = shape, scale = scale)

}