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Diffstat (limited to 'R/ID.R')
| -rw-r--r-- | R/ID.R | 56 |
1 files changed, 27 insertions, 29 deletions
@@ -1,48 +1,46 @@ #' ID method #' -#' This function implements a least squares estimation method of R0 due to Fisman et al. (PloS One, 2013). -#' See details for implementation notes. +#' This function implements a least squares estimation method of R0 due to +#' Fisman et al. (PloS One, 2013). See details for implementation notes. #' -#' The method is based on a straightforward incidence decay model. The estimate of R0 is the value which -#' minimizes the sum of squares between observed case counts and cases counts 'expected' under the model. +#' The method is based on a straightforward incidence decay model. The estimate +#' of R0 is the value which minimizes the sum of squares between observed case +#' counts and cases counts 'expected' under the model. #' -#' 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. #' #' @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 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. #' #' @return \code{ID} returns a single value, the estimate of R0. #' #' @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 -#' ID(NT=NT, mu=5/7) -#' ## obtain Rhat when serial distribution has mean of three days -#' ID(NT=NT, mu=3/7) #' -#' ## ========================================================= ## -#' ## Compute Rhat using only the first five weeks of data ## -#' ## ========================================================= ## +#' # Obtain R0 when the serial distribution has a mean of five days. +#' ID(NT, mu = 5 / 7) #' -#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days +#' # Obtain R0 when the serial distribution has a mean of three days. +#' ID(NT, mu = 3 / 7) #' #' @export ID <- function(NT, mu) { - NT <- as.numeric(NT) - TT <- length(NT) - s <- (1:TT) / mu - y <- log(NT) / s + NT <- as.numeric(NT) + TT <- length(NT) + s <- (1:TT) / mu + y <- log(NT) / s - R0_ID <- exp(sum(y) / TT) + R0_ID <- exp(sum(y) / TT) - return(R0_ID) + return(R0_ID) } |