From b87620843bcae4fc1cb6a9e9caaf52944e93b7b7 Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Fri, 30 Jun 2023 00:04:19 +0000 Subject: Re-gen docs and prevent genning of internal functions --- man/IDEA.Rd | 44 +++++++++++++++++++++----------------------- 1 file changed, 21 insertions(+), 23 deletions(-) (limited to 'man/IDEA.Rd') diff --git a/man/IDEA.Rd b/man/IDEA.Rd index 2dc8240..f4c7d14 100644 --- a/man/IDEA.Rd +++ b/man/IDEA.Rd @@ -9,42 +9,40 @@ IDEA(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{IDEA} 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{ -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 +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. +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 -IDEA(NT=NT, mu=5/7) -## obtain Rhat when serial distribution has mean of three days -IDEA(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. +IDEA(NT, mu = 5 / 7) -IDEA(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. +IDEA(NT, mu = 3 / 7) } -- cgit v1.2.3