diff options
| -rw-r--r-- | R/ID.R | 96 | ||||
| -rw-r--r-- | R/IDEA.R | 116 |
2 files changed, 106 insertions, 106 deletions
@@ -1,48 +1,48 @@ -#' 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.
-#'
-#' 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.
-#'
-#' @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.
-#'
-#' @return \code{ID} returns a single value, the estimate of R0.
-#'
-#' @examples
-#' ## ===================================================== ##
-#' ## Illustrate on 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 ##
-#' ## ========================================================= ##
-#'
-#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
-#'
-#' @export
-ID <- function(NT, mu) {
- NT <- as.numeric(NT)
- TT <- length(NT)
- s <- (1:TT) / mu
- y <- log(NT) / s
-
- R0_ID <- exp(sum(y) / TT)
-
- return(R0_ID)
-}
+#' 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. +#' +#' 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. +#' +#' @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. +#' +#' @return \code{ID} returns a single value, the estimate of R0. +#' +#' @examples +#' ## ===================================================== ## +#' ## Illustrate on 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 ## +#' ## ========================================================= ## +#' +#' ID(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days +#' +#' @export +ID <- function(NT, mu) { + NT <- as.numeric(NT) + TT <- length(NT) + s <- (1:TT) / mu + y <- log(NT) / s + + R0_ID <- exp(sum(y) / TT) + + return(R0_ID) +} @@ -1,58 +1,58 @@ -#' IDEA 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 method is closely related to that implemented in \code{ID}. The method is based on an 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.
-#'
-#' @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.
-#'
-#' @return \code{IDEA} returns a single value, the estimate of R0.
-#'
-#' @examples
-#' ## ===================================================== ##
-#' ## Illustrate on 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
-#' IDEA(NT=NT, mu=5/7)
-#' ## obtain Rhat when serial distribution has mean of three days
-#' IDEA(NT=NT, mu=3/7)
-#'
-#' ## ========================================================= ##
-#' ## Compute Rhat using only the first five weeks of data ##
-#' ## ========================================================= ##
-#'
-#' IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
-#'
-#' @export
-IDEA <- function(NT, mu) {
- if (length(NT) < 2)
- print("Warning: length of NT should be at least two.")
- else {
- NT <- as.numeric(NT)
- TT <- length(NT)
- s <- (1:TT) / mu
-
- y1 <- log(NT) / s
- y2 <- s^2
- y3 <- log(NT)
-
- IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3)
- IDEA2 <- TT * sum(y2) - sum(s)^2
- IDEA <- exp(IDEA1 / IDEA2)
-
- return(IDEA)
- }
-}
+#' IDEA 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 method is closely related to that implemented in \code{ID}. The method is based on an 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. +#' +#' @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. +#' +#' @return \code{IDEA} returns a single value, the estimate of R0. +#' +#' @examples +#' ## ===================================================== ## +#' ## Illustrate on 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 +#' IDEA(NT=NT, mu=5/7) +#' ## obtain Rhat when serial distribution has mean of three days +#' IDEA(NT=NT, mu=3/7) +#' +#' ## ========================================================= ## +#' ## Compute Rhat using only the first five weeks of data ## +#' ## ========================================================= ## +#' +#' IDEA(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days +#' +#' @export +IDEA <- function(NT, mu) { + if (length(NT) < 2) + print("Warning: length of NT should be at least two.") + else { + NT <- as.numeric(NT) + TT <- length(NT) + s <- (1:TT) / mu + + y1 <- log(NT) / s + y2 <- s^2 + y3 <- log(NT) + + IDEA1 <- sum(y2) * sum(y1) - sum(s) * sum(y3) + IDEA2 <- TT * sum(y2) - sum(s)^2 + IDEA <- exp(IDEA1 / IDEA2) + + return(IDEA) + } +} |