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-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/seqB.R
-\name{seqB}
-\alias{seqB}
-\title{seqB method}
-\usage{
-seqB(NT, mu, kappa = 20)
-}
-\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{kappa}{Largest possible value of uniform prior (defaults to 20). This
-describes the prior belief on ranges of R0, and should be set to
-a higher value if R0 is believed to be larger.}
-}
-\value{
-\code{seqB} returns a list containing the following components:
-        \code{Rhat} is the estimate of R0 (the posterior mean),
-        \code{posterior} is the posterior distribution of R0 from which
-        alternate estimates can be obtained (see examples), and \code{group}
-        is an indicator variable (if \code{group == TRUE}, zero values of NT
-        were input and grouping was done to obtain \code{Rhat}). The variable
-        \code{posterior} 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 a sequential Bayesian estimation method of R0 due to
-Bettencourt and Riberio (PloS One, 2008). See details for important
-implementation notes.
-}
-\details{
-The method sets a uniform prior distribution on R0 with possible values
-between zero and \code{kappa}, discretized to a fine grid. The distribution
-of R0 is then updated sequentially, with one update for each new case count
-observation. The final estimate of R0 is \code{Rhat}, the mean of the (last)
-posterior distribution. The prior distribution is the initial belief of the
-distribution of R0, which is the uninformative uniform distribution with
-values between zero and \code{kappa}. Users can change the value of
-\code{kappa} only (i.e., the prior distribution cannot be changed from the
-uniform). As more case counts are observed, the influence of the prior
-distribution should lessen on the final estimate \code{Rhat}.
-
-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.
-
-Our code has been modified to provide an estimate even if case counts equal
-to zero are present in some time intervals. This is done by grouping the
-counts over such periods of time. Without grouping, and in the presence of
-zero counts, no estimate can be provided.
-}
-\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 <- seqB(NT, mu = 5 / 7)
-res1$Rhat
-
-## Obtain R0 when the serial distribution has a mean of three days.
-res2 <- seqB(NT, mu = 3 / 7)
-res2$Rhat
-
-# Compute posterior mode instead of posterior mean and plot.
-
-Rpost <- res1$posterior
-loc <- which(Rpost$pmf == max(Rpost$pmf))
-Rpost$supp[loc] # Posterior mode.
-res1$Rhat # Compare with the posterior mean.
-
-par(mfrow = c(2, 1), mar = c(2, 2, 1, 1))
-
-plot(Rpost$supp, Rpost$pmf, col = "black", type = "l", xlab = "", ylab = "")
-abline(h = 1 / (20 / 0.01 + 1), col = "red")
-abline(v = res1$Rhat, col = "blue")
-abline(v = Rpost$supp[loc], col = "purple")
-legend("topright",
-  legend = c("Prior", "Posterior", "Posterior mean", "Posterior mode"),
-  col = c("red", "black", "blue", "purple"), lty = 1)
-
-}