Split ui.R into components

[Rnaught] / man / ID.Rd

diff --git a/man/ID.Rd b/man/ID.Rd
index 9911f7838212c27ee2987bac4cded1964b26aea7..1d32c50b67dcefa7a295cbf2d9ac394856963eaf 100644 (file)
--- a/man/ID.Rd
+++ b/man/ID.Rd
@@ -9,41 +9,39 @@ ID(NT, mu)
 \arguments{
 \item{NT}{Vector of case counts.}
 
-\item{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.}
+\item{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.}
 }
 \value{
 \code{ID} returns a single value, the estimate of R0.
 }
 \description{
-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.
 }
 \details{
-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.
 }
 \examples{
-## ===================================================== ##
-## Illustrate on weekly data                             ##
-## ===================================================== ##
-
+# 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      ##
-## ========================================================= ##
+# 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)
 
 }