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[Rnaught] / R / IDEA.R

diff --git a/R/IDEA.R b/R/IDEA.R
index 9ca1da67e9721d1116ce40828231827ea3775e5e..53fa653d11672af3d212a4b06dbbcd980437f141 100644 (file)
--- a/R/IDEA.R
+++ b/R/IDEA.R
@@ -1,63 +1,56 @@
 #' 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 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.
 #'
-#'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 recommended. Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.  
+#' @examples
+#' # Weekly data.
+#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
 #'
-#' @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)
+#' # Obtain R0 when the serial distribution has a mean of five days.
+#' IDEA(NT, mu = 5 / 7)
 #'
-#' @return \code{ID} 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}.  
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' IDEA(NT, mu = 3 / 7)
 #'
-#' @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$Rhat
-#' ## obtain Rhat when serial distribution has mean of three days
-#' res2        <- IDEA(NT=NT, mu=3/7)  
-#' res2$Rhat
-#'
-#' ## ========================================================= ##
-#' ## 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
 #' @export
-#'
-\r
-\r
-IDEA <- function(NT, mu){\r
+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)
 
-       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         <-  cumsum(y2)*cumsum(y1)-cumsum(s)*cumsum(y3)
-#        IDEA2         <-      (1:TT)*cumsum(y2)-(cumsum(s))^2 
-#        IDEA          <-      exp(IDEA1/IDEA2)
-#        Rhat          <-      tail(IDEA,1)
-         IDEA1         <-  sum(y2)*sum(y1)-sum(s)*sum(y3)
-         IDEA2         <-      TT*sum(y2)-(sum(s))^2   
-         IDEA          <-      exp(IDEA1/IDEA2)
-         
+    IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)
+    IDEA2 <- TT * sum(y2) - sum(s)^2
+    IDEA <- exp(IDEA1 / IDEA2)
 
-         return(list(Rhat=IDEA, inputs=list(NT=NT, mu=mu)))
-         }\r
-}\r
+    return(IDEA)
+  }
+}