Re-style code and enforce 80 character line limit

[Rnaught] / R / ID.R

diff --git a/R/ID.R b/R/ID.R
index 0e3cc35bafb58e0e959f51b7acdb527f125295b4..7e8a04d46796b4cacb74675b8525b3590ac11eec 100644 (file)
--- a/R/ID.R
+++ b/R/ID.R
@@ -1,48 +1,46 @@
 #' ID method
 #'
 #' 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.
+#' 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.
+#' 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.
+#' 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 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.
+#' @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
 #'
 #' @return \code{ID} returns a single value, the estimate of R0.
 #'
 #' @examples

-#' ## ===================================================== ##
-#' ## Illustrate on weekly data                             ##
-#' ## ===================================================== ##
-#'
+#' # Weekly data:

 #' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
 #' 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      ##
-#' ## ========================================================= ##
+#' # Obtain R0 when the serial distribution has a mean of five days.
+#' ID(NT, mu = 5 / 7)

 #'
 #'

-#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' ID(NT, mu = 3 / 7)

 #'
 #' @export
 ID <- function(NT, mu) {
 #'
 #' @export
 ID <- function(NT, mu) {

-    NT <- as.numeric(NT)
-    TT <- length(NT)
-    s <- (1:TT) / mu
-    y <- log(NT) / s
+  NT <- as.numeric(NT)
+  TT <- length(NT)
+  s <- (1:TT) / mu
+  y <- log(NT) / s

 
 

-    R0_ID <- exp(sum(y) / TT)
+  R0_ID <- exp(sum(y) / TT)

 
 

-    return(R0_ID)
+  return(R0_ID)

 }
 }