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 -------------------------------------------------------------- man/wp.Rd | 103 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 103 insertions(+), 103 deletions(-) delete mode 100644 man/WP.Rd create mode 100644 man/wp.Rd (limited to 'man') 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) - -} diff --git a/man/wp.Rd b/man/wp.Rd new file mode 100644 index 0000000..479593b --- /dev/null +++ b/man/wp.Rd @@ -0,0 +1,103 @@ +% 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