Re-style code and enforce 80 character line limit

[Rnaught] / R / ID.R

diff --git a/R/ID.R b/R/ID.R
index b5fc8c2e78c7139aa4e696510bc3bf34e1921400..7e8a04d46796b4cacb74675b8525b3590ac11eec 100644 (file)
--- a/R/ID.R
+++ b/R/ID.R
@@ -1,49 +1,46 @@
-#' ID 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
-#' The method is based on a straightforward incidence decay model. The estimate of R0 is the value which\r
-#' minimizes the sum of squares between observed case counts and cases counts '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{ID} 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
-#' ## 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
-#' ID(NT=NT, mu=5/7)\r
-#' ## obtain Rhat when serial distribution has mean of three days\r
-#' ID(NT=NT, mu=3/7)\r
-#'\r
-#' ## ========================================================= ##\r
-#' ## Compute Rhat using only the first five weeks of data      ##\r
-#' ## ========================================================= ##\r
-#'\r
-#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days\r
-#'\r
-#' @export\r
-ID <- function(NT, mu) {\r
-    NT <- as.numeric(NT)\r
-    TT <- length(NT)\r
-    s <- (1:TT) / mu\r
-    y <- log(NT) / s\r
-\r
-    R0_ID <- exp(sum(y) / TT)\r
-\r
-    return(R0_ID)\r
-}\r
+#' ID method
+#'
+#' This function implements a least squares estimation method of R0 due to
+#' Fisman et al. (PloS One, 2013). See details for implementation notes.
+#'
+#' The method is based on a straightforward 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{ID} 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.
+#' ID(NT, mu = 5 / 7)
+#'
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' ID(NT, mu = 3 / 7)
+#'
+#' @export
+ID <- function(NT, mu) {
+  NT <- as.numeric(NT)
+  TT <- length(NT)
+  s <- (1:TT) / mu
+  y <- log(NT) / s
+
+  R0_ID <- exp(sum(y) / TT)
+
+  return(R0_ID)
+}