From f5fcb4e1d46bfe8dc2d79cf4f3022f964b08a321 Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Sat, 13 Jan 2024 10:55:48 +0000 Subject: Rename seqB --- man/seqB.Rd | 91 -------------------------------------------------------- man/seq_bayes.Rd | 91 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 91 insertions(+), 91 deletions(-) delete mode 100644 man/seqB.Rd create mode 100644 man/seq_bayes.Rd (limited to 'man') diff --git a/man/seqB.Rd b/man/seqB.Rd deleted file mode 100644 index 0864294..0000000 --- a/man/seqB.Rd +++ /dev/null @@ -1,91 +0,0 @@ -% 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) - -} diff --git a/man/seq_bayes.Rd b/man/seq_bayes.Rd new file mode 100644 index 0000000..0864294 --- /dev/null +++ b/man/seq_bayes.Rd @@ -0,0 +1,91 @@ +% 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) + +} -- cgit v1.2.3