From 4b5ae82bda701efe4ae19dfdda1a9e1f69dd35ea Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Sun, 11 Feb 2024 01:17:20 +0000 Subject: Rename WP --- man/WP.Rd | 103 -------------------------------------------------------------- 1 file changed, 103 deletions(-) delete mode 100644 man/WP.Rd (limited to 'man/WP.Rd') diff --git a/man/WP.Rd b/man/WP.Rd deleted file mode 100644 index 479593b..0000000 --- a/man/WP.Rd +++ /dev/null @@ -1,103 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/WP.R -\name{WP} -\alias{WP} -\title{WP method} -\usage{ -WP( - NT, - mu = NA, - search = list(B = 100, shape.max = 10, scale.max = 10), - tol = 0.999 -) -} -\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{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).} -} -\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). -} -\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. -} -\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). - -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, -and is caused by the discretization. -} -\examples{ -# Weekly data. -NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) - -# Obtain R0 when the serial distribution has a mean of five days. -res1 <- WP(NT, mu = 5 / 7) -res1$Rhat - -# Obtain R0 when the serial distribution has a mean of three days. -res2 <- WP(NT, mu = 3 / 7) -res2$Rhat - -# Obtain R0 when the serial distribution is unknown. -# NOTE: This implementation will take longer to run. -res3 <- WP(NT) -res3$Rhat - -# Find the mean of the estimated serial distribution. -serial <- res3$SD -sum(serial$supp * serial$pmf) - -} -- cgit v1.2.3