From e1c61de5a0e693e2f24a1c4a10336e2a1c4563cb Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Wed, 10 Jan 2024 14:50:22 +0000 Subject: Rename ID and IDEA --- R/ID.R | 46 ---------------------------------------------- R/IDEA.R | 56 -------------------------------------------------------- R/id.R | 46 ++++++++++++++++++++++++++++++++++++++++++++++ R/idea.R | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 102 insertions(+), 102 deletions(-) delete mode 100644 R/ID.R delete mode 100644 R/IDEA.R create mode 100644 R/id.R create mode 100644 R/idea.R (limited to 'R') diff --git a/R/ID.R b/R/ID.R deleted file mode 100644 index 7e8a04d..0000000 --- a/R/ID.R +++ /dev/null @@ -1,46 +0,0 @@ -#' 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 -#' # 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. -#' ID(NT, mu = 5 / 7) -#' -#' # 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 - - R0_ID <- exp(sum(y) / TT) - - return(R0_ID) -} diff --git a/R/IDEA.R b/R/IDEA.R deleted file mode 100644 index 53fa653..0000000 --- a/R/IDEA.R +++ /dev/null @@ -1,56 +0,0 @@ -#' 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 -#' # 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. -#' IDEA(NT, mu = 5 / 7) -#' -#' # Obtain R0 when the serial distribution has a mean of three days. -#' IDEA(NT, mu = 3 / 7) -#' -#' @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) - } -} diff --git a/R/id.R b/R/id.R new file mode 100644 index 0000000..7e8a04d --- /dev/null +++ b/R/id.R @@ -0,0 +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. +#' +#' 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 +#' # 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. +#' ID(NT, mu = 5 / 7) +#' +#' # 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 + + R0_ID <- exp(sum(y) / TT) + + return(R0_ID) +} diff --git a/R/idea.R b/R/idea.R new file mode 100644 index 0000000..53fa653 --- /dev/null +++ b/R/idea.R @@ -0,0 +1,56 @@ +#' 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 +#' # 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. +#' IDEA(NT, mu = 5 / 7) +#' +#' # Obtain R0 when the serial distribution has a mean of three days. +#' IDEA(NT, mu = 3 / 7) +#' +#' @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) + } +} -- cgit v1.2.3