From 80df3ed7a280f86a3b9b5443309487d428f4fe95 Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Thu, 29 Jun 2023 23:47:01 +0000 Subject: Re-style code and enforce 80 character line limit --- R/seqB.R | 201 ++++++++++++++++++++++++++++++++------------------------------- 1 file changed, 102 insertions(+), 99 deletions(-) (limited to 'R/seqB.R') diff --git a/R/seqB.R b/R/seqB.R index 8685f39..e51117f 100644 --- a/R/seqB.R +++ b/R/seqB.R @@ -1,126 +1,129 @@ #' seqB method #' -#' This function implements a sequential Bayesian estimation method of R0 due to Bettencourt and Riberio (PloS One, 2008). -#' See details for important implementation notes. +#' This function implements a sequential Bayesian estimation method of R0 due to +#' Bettencourt and Riberio (PloS One, 2008). See details for important +#' implementation notes. #' -#' 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 in this implementation 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}. +#' 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. +#' 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. +#' 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. #' #' @param NT Vector of case counts. -#' @param 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. -#' @param 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. +#' @param 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. +#' @param 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. #' -#' @return \code{secB} 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). +#' @return \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). #' #' @examples -#' ## ===================================================== ## -#' ## Illustrate on weekly data ## -#' ## ===================================================== ## -#' +#' # Weekly data. #' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) -#' ## obtain Rhat when serial distribution has mean of five days -#' res1 <- seqB(NT=NT, mu=5/7) +#' +#' ## Obtain R0 when the serial distribution has a mean of five days. +#' res1 <- seqB(NT, mu = 5 / 7) #' res1$Rhat -#' ## obtain Rhat when serial distribution has mean of three days -#' res2 <- seqB(NT=NT, mu=3/7) +#' +#' ## Obtain R0 when the serial distribution has a mean of three days. +#' res2 <- seqB(NT, mu = 3 / 7) #' res2$Rhat #' -#' ## ============================================================= ## -#' ## Compute posterior mode instead of posterior mean and plot ## -#' ## ============================================================= ## +#' # Compute posterior mode instead of posterior mean and plot. #' -#' Rpost <- res1$posterior +#' Rpost <- res1$posterior #' loc <- which(Rpost$pmf == max(Rpost$pmf)) -#' Rpost$supp[loc] # posterior mode -#' res1$Rhat # compare with posterior mean +#' 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 (Rhat)", "posterior mode"), -#' col=c("red", "black", "blue", "purple"), lty=1) -#' plot(Rpost$supp, Rpost$pmf, col="black", type="l", xlim=c(0.5, 1.5), 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 (Rhat)", "posterior mode"), -#' col=c("red", "black", "blue", "purple"), lty=1) +#' par(mfrow = c(2, 1), mar = c(2, 2, 1, 1)) #' -#' ## ========================================================= ## -#' ## Compute Rhat using only the first five weeks of data ## -#' ## ========================================================= ## -#' -#' res3 <- seqB(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days -#' res3$Rhat +#' plot(Rpost$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) #' #' @export -seqB <- function(NT, mu, kappa=20) { - if (length(NT) < 2) - print("Warning: length of NT should be at least two.") - else { - if (min(NT) > 0) { - times <- 1:length(NT) - tau <- diff(times) - } - group <- FALSE - if (min(NT) == 0) { - times <- which(NT > 0) - NT <- NT[times] - tau <- diff(times) - group <- TRUE - } +seqB <- function(NT, mu, kappa = 20) { + if (length(NT) < 2) { + print("Warning: length of NT should be at least two.") + } else { + if (min(NT) > 0) { + times <- 1:length(NT) + tau <- diff(times) + } + group <- FALSE + if (min(NT) == 0) { + times <- which(NT > 0) + NT <- NT[times] + tau <- diff(times) + group <- TRUE + } - R <- seq(0, kappa, 0.01) - prior0 <- rep(1, kappa / 0.01 + 1) - prior0 <- prior0 / sum(prior0) - k <- length(NT) - 1 - R0.post <- matrix(0, nrow=k, ncol=length(R)) - prior <- prior0 - posterior <- seq(0, length(prior0)) - gamma <- 1 / mu + R <- seq(0, kappa, 0.01) + prior0 <- rep(1, kappa / 0.01 + 1) + prior0 <- prior0 / sum(prior0) + k <- length(NT) - 1 + R0.post <- matrix(0, nrow = k, ncol = length(R)) + prior <- prior0 + posterior <- seq(0, length(prior0)) + gamma <- 1 / mu - for (i in 1:k) { - mm1 <- NT[i] - mm2 <- NT[i+1] - lambda <- tau[i] * gamma * (R - 1) - lambda <- log(mm1) + lambda - loglik <- mm2 * lambda - exp(lambda) - maxll <- max(loglik) - const <- 0 + for (i in 1:k) { + mm1 <- NT[i] + mm2 <- NT[i + 1] + lambda <- tau[i] * gamma * (R - 1) + lambda <- log(mm1) + lambda + loglik <- mm2 * lambda - exp(lambda) + maxll <- max(loglik) + const <- 0 - if (maxll > 700) - const <- maxll - 700 + if (maxll > 700) + const <- maxll - 700 - loglik <- loglik-const - posterior <- exp(loglik) * prior - posterior <- posterior / sum(posterior) - prior <- posterior - } + loglik <- loglik - const + posterior <- exp(loglik) * prior + posterior <- posterior / sum(posterior) + prior <- posterior + } - Rhat <- sum(R * posterior) + Rhat <- sum(R * posterior) - return(list(Rhat=Rhat, posterior=list(supp=R, pmf=posterior), group=group)) - } + return(list(Rhat = Rhat, + posterior = list(supp = R, pmf = posterior), + group = group)) + } } -- cgit v1.2.3