-#' IDEA method\r
-#'\r
-#' This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013).\r
-#' See details for implementation notes.\r
-#'\r
-#' This method is closely related to that implemented in \code{ID}. The method is based on an incidence decay model.\r
-#' The estimate of R0 is the value which minimizes the sum of squares between observed case counts and cases counts\r
-#' expected under the model.\r
-#'\r
-#' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.\r
-#' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.\r
-#' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.\r
-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.\r
-#'\r
-#' @param NT Vector of case counts\r
-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are\r
-#' weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case\r
-#' counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)\r
-#'\r
-#' @return \code{IDEA} returns a list containing the following components: \code{Rhat} is the estimate of R0 and\r
-#' \code{inputs} is a list of the original input variables \code{NT, mu}.\r
-#'\r
-#' @examples\r
-#'\r
-#' ## ===================================================== ##\r
-#' ## Illustrate on weekly data ##\r
-#' ## ===================================================== ##\r
-#'\r
-#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)\r
-#' ## obtain Rhat when serial distribution has mean of five days\r
-#' res1 <- IDEA(NT=NT, mu=5/7)\r
-#' res1$Rhat\r
-#' ## obtain Rhat when serial distribution has mean of three days\r
-#' res2 <- IDEA(NT=NT, mu=3/7)\r
-#' res2$Rhat\r
-#'\r
-#' ## ========================================================= ##\r
-#' ## Compute Rhat using only the first five weeks of data ##\r
-#' ## ========================================================= ##\r
-#'\r
-#'\r
-#' res3 <- IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days\r
-#' res3$Rhat\r
-#'\r
-#' @export\r
-IDEA <- function(NT, mu) {\r
- if (length(NT) < 2)\r
- print("Warning: length of NT should be at least two.")\r
- else {\r
- NT <- as.numeric(NT)\r
- TT <- length(NT)\r
- s <- (1:TT) / mu\r
-\r
- y1 <- log(NT) / s\r
- y2 <- s^2\r
- y3 <- log(NT)\r
-\r
- IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)\r
- IDEA2 <- TT * sum(y2) - sum(s)^2\r
- IDEA <- exp(IDEA1 / IDEA2)\r
-\r
- return(list(Rhat=IDEA, inputs=list(NT=NT, mu=mu)))\r
- }\r
-}\r
+#' IDEA method
+#'
+#' This function implements a least squares estimation method of R0 due to
+#' Fisman et al. (PloS One, 2013). See details for implementation notes.
+#'
+#' This method is closely related to that implemented in \code{ID}. The method
+#' is based on an incidence decay model. The estimate of R0 is the value which
+#' minimizes the sum of squares between observed case counts and cases counts
+#' expected under the model.
+#'
+#' 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.
+#'
+#' @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 single value, the estimate of R0.
+#'
+#' @examples
+#' # Weekly data.
+#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
+#'
+#' # Obtain R0 when the serial distribution has a mean of five days.
+#' IDEA(NT, mu = 5 / 7)
+#'
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' IDEA(NT, mu = 3 / 7)
+#'
+#' @export
+IDEA <- function(NT, mu) {
+ if (length(NT) < 2)
+ print("Warning: length of NT should be at least two.")
+ else {
+ NT <- as.numeric(NT)
+ TT <- length(NT)
+ s <- (1:TT) / mu
+
+ y1 <- log(NT) / s
+ y2 <- s^2
+ y3 <- log(NT)
+
+ IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)
+ IDEA2 <- TT * sum(y2) - sum(s)^2
+ IDEA <- exp(IDEA1 / IDEA2)
+
+ return(IDEA)
+ }
+}
git.nmode.ca / Rnaught / blobdiff
