X-Git-Url: https://git.nmode.ca/Rnaught/blobdiff_plain/9c1a5668803e735f034700c55028ffc0146f1e93..5f4889d4df5e94f194ef7f8b839496db04b17f4e:/R/IDEA.R?ds=sidebyside

diff --git a/R/IDEA.R b/R/IDEA.R
index 7668540..20a9401 100644
--- a/R/IDEA.R
+++ b/R/IDEA.R
@@ -10,37 +10,31 @@
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.
 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.
 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.
-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.
+#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.
 #'
-#' @param NT Vector of case counts
-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are
-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)
+#' @param NT Vector of case counts.
+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts
+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one. If case
+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.
 #'
-#' @return \code{IDEA} returns a list containing the following components: \code{Rhat} is the estimate of R0 and
-#'         \code{inputs} is a list of the original input variables \code{NT, mu}.
+#' @return \code{IDEA} returns a single value, the estimate of R0.
 #'
 #' @examples
-#'
 #' ## ===================================================== ##
 #' ## Illustrate on weekly data                             ##
 #' ## ===================================================== ##
 #'
 #' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
 #' ## obtain Rhat when serial distribution has mean of five days
-#' res1 <- IDEA(NT=NT, mu=5/7)
-#' res1$Rhat
+#' IDEA(NT=NT, mu=5/7)
 #' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- IDEA(NT=NT, mu=3/7)
-#' res2$Rhat
+#' IDEA(NT=NT, mu=3/7)
 #'
 #' ## ========================================================= ##
 #' ## Compute Rhat using only the first five weeks of data      ##
 #' ## ========================================================= ##
 #'
-#'
-#' res3 <- IDEA(NT=NT[1:5], mu=5/7)		# serial distribution has mean of five days
-#' res3$Rhat
+#' IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
 #'
 #' @export
 IDEA <- function(NT, mu) {
@@ -59,6 +53,6 @@ IDEA <- function(NT, mu) {
         IDEA2 <- TT * sum(y2) - sum(s)^2
         IDEA <- exp(IDEA1 / IDEA2)
 
-        return(list(Rhat=IDEA, inputs=list(NT=NT, mu=mu)))
+        return(IDEA)
     }
 }