Refactor seqB

1 files changed, 54 insertions, 53 deletions

diff --git a/man/seq_bayes.Rd b/man/seq_bayes.Rd
index 0864294..41445a9 100644
--- a/man/seq_bayes.Rd
+++ b/man/seq_bayes.Rd
@@ -1,34 +1,41 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/seqB.R
-\name{seqB}
-\alias{seqB}
-\title{seqB method}
+% Please edit documentation in R/seq_bayes.R
+\name{seq_bayes}
+\alias{seq_bayes}
+\title{Sequential Bayes (seqB)}
 \usage{
-seqB(NT, mu, kappa = 20)
+seq_bayes(cases, mu, kappa = 20, post = FALSE)
 }
 \arguments{
-\item{NT}{Vector of case counts.}
+\item{cases}{Vector of case counts. The vector must only contain non-negative
+integers, and have at least two positive integers.}
 
-\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 must be a positive number.
+The value should match the 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 \code{1}. If case counts are daily and the serial
+distribution has a mean of seven days, then \code{mu} should be set to \code{7}.}
 
-\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.}
+\item{kappa}{Largest possible value of the uniform prior (defaults to \code{20}).
+This must be a number greater than or equal to \code{1}. It describes the prior
+belief on the ranges of R0, and should be set to a higher value if R0 is
+believed to be larger.}
+
+\item{post}{Whether to return the posterior distribution of R0 instead of the
+estimate of R0 (defaults to \code{FALSE}). This must be a value identical to
+\code{TRUE} or \code{FALSE}.}
 }
 \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).
+If \code{post} is identical to \code{TRUE}, a list containing the following
+components is returned:
+\itemize{
+\item \code{supp} - the support of the posterior distribution of R0
+\item \code{pmf} - the probability mass function of the posterior distribution
+}
+
+Otherwise, if \code{post} is identical to \code{FALSE}, only the estimate of R0 is
+returned. Note that the estimate is equal to \code{sum(supp * pmf)} (i.e., the
+posterior mean).
 }
 \description{
 This function implements a sequential Bayesian estimation method of R0 due to
@@ -37,15 +44,15 @@ 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
+between \code{0} 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 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}.
+values between \code{0} 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.
 
 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
@@ -61,31 +68,25 @@ zero counts, no estimate can be provided.
 }
 \examples{
 # Weekly data.
-NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
+cases <- 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 five days.
+seq_bayes(cases, mu = 5 / 7)
 
-## Obtain R0 when the serial distribution has a mean of three days.
-res2 <- seqB(NT, mu = 3 / 7)
-res2$Rhat
+# Obtain R0 when the serial distribution has a mean of three days.
+seq_bayes(cases, mu = 3 / 7)
 
-# Compute posterior mode instead of posterior mean and plot.
+# Obtain R0 when the serial distribution has a mean of seven days, and R0 is
+# believed to be at most 4.
+estimate <- seq_bayes(cases, mu = 1, kappa = 4)
 
-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)
+# Same as above, but return the posterior distribution instead of the
+# estimate.
+posterior <- seq_bayes(cases, mu = 1, kappa = 4, post = TRUE)
 
+# Note that the following always holds:
+estimate == sum(posterior$supp * posterior$pmf)
+}
+\references{
+\href{https://doi.org/10.1371/journal.pone.0002185}{Bettencourt and Riberio (PloS One, 2008)}
 }