Remove 'shinyjs' dependency

[Rnaught] / R / ID.R

diff --git a/R/ID.R b/R/ID.R
index 7b0a1b97815cce42f9606c51aa57cd4dc685809d..0e3cc35bafb58e0e959f51b7acdb527f125295b4 100644 (file)
--- a/R/ID.R
+++ b/R/ID.R
@@ -1,54 +1,48 @@
-#' 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
-#' ## ===================================================== ##\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 <- ID(NT=NT, mu=5/7)\r
-#' res1$Rhat\r
-#' ## obtain Rhat when serial distribution has mean of three days\r
-#' res2        <- ID(NT=NT, mu=3/7)\r
-#' res2$Rhat\r
-#'\r
-#' ## ========================================================= ##\r
-#' ## Compute Rhat using only the first five weeks of data      ##\r
-#' ## ========================================================= ##\r
-#'\r
-#'\r
-#' res3 <- ID(NT=NT[1:5], mu=5/7)              # serial distribution has mean of five days\r
-#' res3$Rhat\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(list=c(Rhat=R0_ID, inputs=list(NT=NT, mu=mu)))\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
+#' ## ===================================================== ##
+#' ## 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
+#' ID(NT=NT, mu=5/7)
+#' ## obtain Rhat when serial distribution has mean of three days
+#' ID(NT=NT, mu=3/7)
+#'
+#' ## ========================================================= ##
+#' ## Compute Rhat using only the first five weeks of data      ##
+#' ## ========================================================= ##
+#'
+#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
+#'
+#' @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)
+}