Rename ID and IDEA

1 files changed, 0 insertions, 56 deletions

diff --git a/R/IDEA.R b/R/IDEA.R
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index 53fa653..0000000
--- a/R/IDEA.R
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-#' 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)
-  }
-}