From e0988851b26668ec69131e22a0815ce6f85b94c4 Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Wed, 21 Jun 2023 09:09:08 +0000 Subject: Edit docs --- R/seqB.R | 33 ++++++++++++++++----------------- 1 file changed, 16 insertions(+), 17 deletions(-) (limited to 'R/seqB.R') diff --git a/R/seqB.R b/R/seqB.R index 7938311..a64b598 100644 --- a/R/seqB.R +++ b/R/seqB.R @@ -7,43 +7,42 @@ #' 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 kappa only (ie. the prior distribution +#' 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 +#' 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. #' -#' @param NT Vector of case counts -#' @param 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, 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, -#' so should be set to a higher value if R0 is believed to be larger. +#' @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. #' -#' @return 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), -#' \code{group} is an indicator variable (if \code{group=TRUE}, zero values of NT were input and grouping was done to -#' obtain \code{Rhat}), and \code{inputs} is a list of the original input variables \code{NT, gamma, kappa}. 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{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). #' #' @examples #' ## ===================================================== ## #' ## Illustrate on weekly data ## #' ## ===================================================== ## #' -#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) +#' 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) +#' res1 <- seqB(NT=NT, mu=5/7) #' res1$Rhat #' ## obtain Rhat when serial distribution has mean of three days -#' res2 <- seqB(NT=NT, mu=3/7) +#' res2 <- seqB(NT=NT, mu=3/7) #' res2$Rhat #' #' ## ============================================================= ## -- cgit v1.2.3