Re-gen docs and prevent genning of internal functions

[Rnaught] / man / WP.Rd

diff --git a/man/WP.Rd b/man/WP.Rd
index 13471ca11bb83848e1b1bb170957dedc0351535c..479593bab331d1c375d4413a3f5b262a760d91d8 100644 (file)
--- a/man/WP.Rd
+++ b/man/WP.Rd
@@ -14,81 +14,90 @@ WP(
 \arguments{
 \item{NT}{Vector of case counts.}
 
 \arguments{
 \item{NT}{Vector of case counts.}
 

-\item{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). The default value of \code{mu} is set to \code{NA}.}
+\item{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). The default value of \code{mu} is set to \code{NA}.}

 
 

-\item{search}{List of default values for the grid search algorithm. The list includes three elements: the
-first is \code{B}, which is the length of the grid in one dimension; the second is
-\code{scale.max}, which is the largest possible value of the scale parameter; and the third
-is \code{shape.max}, which is the largest possible value of the shape parameter. Defaults to
-\code{B=100, scale.max=10, shape.max=10}. For both shape and scale, the smallest possible
-value is 1/\code{B}.}
+\item{search}{List of default values for the grid search algorithm. The list
+includes three elements: the first is \code{B}, which is the
+length of the grid in one dimension; the second is
+\code{scale.max}, which is the largest possible value of the
+scale parameter; and the third is \code{shape.max}, which is
+the largest possible value of the shape parameter. Defaults to
+\code{B = 100, scale.max = 10, shape.max = 10}. For both shape
+and scale, the smallest possible value is 1/\code{B}.}

 
 

-\item{tol}{Cutoff value for cumulative distribution function of the pre-discretization gamma serial
-distribution. Defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the
-original gamma distribution has cumulative probability of no more than 0.999 at this maximum).}
+\item{tol}{Cutoff value for cumulative distribution function of the
+pre-discretization gamma serial distribution. Defaults to 0.999
+(i.e. in the discretization, the maximum is chosen such that the
+original gamma distribution has cumulative probability of no more
+than 0.999 at this maximum).}

 }
 \value{
 }
 \value{

-\code{WP} returns a list containing the following components:  \code{Rhat} is the estimate of R0,
-        and \code{SD} is either the discretized serial distribution (if \code{mu} is not \code{NA}), or the
-        estimated discretized serial distribution (if \code{mu} is \code{NA}). The list also returns the
-        variable \code{check}, which is equal to the number of non-unique maximum likelihood estimators.
-        The serial distribution \code{SD} is returned as a list made up of \code{supp} (the support of
-        the distribution) and \code{pmf} (the probability mass function).
+\code{WP} returns a list containing the following components:
+        \code{Rhat} is the estimate of R0, and \code{SD} is either the
+        discretized serial distribution (if \code{mu} is not \code{NA}), or
+        the estimated discretized serial distribution (if \code{mu} is
+        \code{NA}). The list also returns the variable \code{check}, which is
+        equal to the number of non-unique maximum likelihood estimators. The
+        serial distribution \code{SD} is returned as a list made up of
+        \code{supp} (the support of the distribution) and \code{pmf} (the
+        probability mass function).

 }
 \description{
 }
 \description{

-This function implements an R0 estimation due to White and Pagano (Statistics in Medicine, 2008).
-The method is based on maximum likelihood estimation in a Poisson transmission model.
-See details for important implementation notes.
+This function implements an R0 estimation due to White and Pagano (Statistics
+in Medicine, 2008). The method is based on maximum likelihood estimation in a
+Poisson transmission model. See details for important implementation notes.

 }
 \details{
 }
 \details{

-This method is based on a Poisson transmission model, and hence may be most most valid at the beginning
-of an epidemic. In their model, the serial distribution is assumed to be discrete with a finite number
-of posible values. In this implementation, if \code{mu} is not {NA}, the serial distribution is taken to
-be a discretized version of a gamma distribution with mean \code{mu}, shape parameter one, and largest
-possible value based on parameter \code{tol}. When \code{mu} is \code{NA}, the function implements a
-grid search algorithm to find the maximum likelihood estimator over all possible gamma distributions
-with unknown mean and variance, restricting these to a prespecified grid (see \code{search} parameter).  
+This method is based on a Poisson transmission model, and hence may be most
+most valid at the beginning of an epidemic. In their model, the serial
+distribution is assumed to be discrete with a finite number of posible
+values. In this implementation, if \code{mu} is not {NA}, the serial
+distribution is taken to be a discretized version of a gamma distribution
+with mean \code{mu}, shape parameter one, and largest possible value based on
+parameter \code{tol}. When \code{mu} is \code{NA}, the function implements a
+grid search algorithm to find the maximum likelihood estimator over all
+possible gamma distributions with unknown mean and variance, restricting
+these to a prespecified grid (see \code{search} parameter).

 
 

-When the serial distribution is known (i.e., \code{mu} is not \code{NA}), sensitivity testing of \code{mu}
-is strongly recommended. If the serial distribution is unknown (i.e., \code{mu} is \code{NA}), the
-likelihood function can be flat near the maximum, resulting in numerical instability of the optimizer.
-When \code{mu} is \code{NA}, the implementation takes considerably longer to run. Users should be careful
-about units of time (e.g., are counts observed daily or weekly?) when implementing.  
+When the serial distribution is known (i.e., \code{mu} is not \code{NA}),
+sensitivity testing of \code{mu} is strongly recommended. If the serial
+distribution is unknown (i.e., \code{mu} is \code{NA}), the likelihood
+function can be flat near the maximum, resulting in numerical instability of
+the optimizer. When \code{mu} is \code{NA}, the implementation takes
+considerably longer to run. Users should be careful about units of time
+(e.g., are counts observed daily or weekly?) when implementing.

 
 

-The model developed in White and Pagano (2008) is discrete, and hence the serial distribution is finite 
-discrete. In our implementation, the input value \code{mu} is that of a continuous distribution. The
-algorithm discretizes this input when \code{mu} is not \code{NA}, and hence the mean of the serial
-distribution returned in the list \code{SD} will differ from \code{mu} somewhat. That is to say, if the
-user notices that the input \code{mu} and output mean of \code{SD} are different, this is to be expected,
+The model developed in White and Pagano (2008) is discrete, and hence the
+serial distribution is finite discrete. In our implementation, the input
+value \code{mu} is that of a continuous distribution. The algorithm
+discretizes this input when \code{mu} is not \code{NA}, and hence the mean of
+the serial distribution returned in the list \code{SD} will differ from
+\code{mu} somewhat. That is to say, if the user notices that the input
+\code{mu} and output mean of \code{SD} are different, this is to be expected,

 and is caused by the discretization.
 }
 \examples{
 and is caused by the discretization.
 }
 \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
-res1 <- WP(NT=NT, mu=5/7)
+
+# Obtain R0 when the serial distribution has a mean of five days.
+res1 <- WP(NT, mu = 5 / 7)

 res1$Rhat
 res1$Rhat

-## obtain Rhat when serial distribution has mean of three days
-res2   <- WP(NT=NT, mu=3/7)
+
+# Obtain R0 when the serial distribution has a mean of three days.
+res2 <- WP(NT, mu = 3 / 7)

 res2$Rhat
 res2$Rhat

-## obtain Rhat when serial distribution is unknown
-## NOTE:  this implementation will take longer to run
-res3   <- WP(NT=NT)
+
+# Obtain R0 when the serial distribution is unknown.
+# NOTE: This implementation will take longer to run.
+res3 <- WP(NT)

 res3$Rhat
 res3$Rhat

-## find mean of estimated serial distribution
+
+# Find the mean of the estimated serial distribution.

 serial <- res3$SD
 sum(serial$supp * serial$pmf)
 
 serial <- res3$SD
 sum(serial$supp * serial$pmf)
 

-## ========================================================= ##
-## Compute Rhat using only the first five weeks of data      ##
-## ========================================================= ##
-
-res4 <- WP(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
-res4$Rhat
-

 }
 }