Group WP-related functions

[Rnaught] / R / WP_internal.R

diff --git a/R/WP_internal.R b/R/WP_internal.R
deleted file mode 100644 (file)
index dd10d29..0000000
--- a/R/WP_internal.R
+++ /dev/null
@@ -1,116 +0,0 @@
-#' WP method background function WP_known
-#'
-#' This is a background/internal function called by \code{WP}. It computes the
-#' maximum likelihood estimator of R0 assuming that the serial distribution is
-#' known and finite discrete.
-#'
-#' @param NT Vector of case counts.
-#' @param p Discretized version of the serial distribution.
-#'
-#' @return The function returns the maximum likelihood estimator of R0.
-#'
-#' @noRd
-WP_known <- function(NT, p) {
-  k <- length(p)
-  TT <- length(NT) - 1
-  mu_t <- rep(0, TT)
-
-  for (i in 1:TT) {
-    Nt <- NT[i:max(1, i - k + 1)]
-    mu_t[i] <- sum(p[1:min(k, i)] * Nt)
-  }
-
-  Rhat <- sum(NT[-1]) / sum(mu_t)
-  return(Rhat)
-}
-
-#' WP method background function WP_unknown
-#'
-#' This is a background/internal function called by \code{WP}. It computes the
-#' maximum likelihood estimator of R0 assuming that the serial distribution is
-#' unknown but comes from a discretized gamma distribution. The function then
-#' implements a simple grid search algorithm to obtain the maximum likelihood
-#' estimator of R0 as well as the gamma parameters.
-#'
-#' @param NT Vector of case counts.
-#' @param B Length of grid for shape and scale (grid search parameter).
-#' @param shape.max Maximum shape value (grid \code{search} parameter).
-#' @param scale.max Maximum scale value (grid \code{search} parameter).
-#' @param tol cutoff value for cumulative distribution function of the serial
-#'            distribution (defaults to 0.999).
-#'
-#' @return The function returns \code{Rhat}, the maximum likelihood estimator of
-#'         R0, as well as the maximum likelihood estimator of the discretized
-#'         serial distribution given by \code{p} (the probability mass function)
-#'         and \code{range.max} (the distribution has support on the integers
-#'         one to \code{range.max}). The function also returns \code{resLL} (all
-#'         values of the log-likelihood) at \code{shape} (grid for shape
-#'         parameter) and at \code{scale} (grid for scale parameter), as well as
-#'         \code{resR0} (the full vector of maximum likelihood estimators),
-#'         \code{JJ} (the locations for the likelihood for these), and \code{J0}
-#'         (the location for the maximum likelihood estimator \code{Rhat}). If
-#'         \code{JJ} and \code{J0} are not the same, this means that the maximum
-#'         likelihood estimator is not unique.
-#'
-#' @importFrom stats pgamma qgamma
-#'
-#' @noRd
-WP_unknown <- function(NT, B = 100, shape.max = 10, scale.max = 10,
-                       tol = 0.999) {
-  shape <- seq(0, shape.max, length.out = B + 1)
-  scale <- seq(0, scale.max, length.out = B + 1)
-  shape <- shape[-1]
-  scale <- scale[-1]
-
-  resLL <- matrix(0, B, B)
-  resR0 <- matrix(0, B, B)
-
-  for (i in 1:B)
-    for (j in 1:B) {
-      range.max <- ceiling(qgamma(tol, shape = shape[i], scale = scale[j]))
-      p <- diff(pgamma(0:range.max, shape = shape[i], scale = scale[j]))
-      p <- p / sum(p)
-      mle <- WP_known(NT, p)
-      resLL[i, j] <- computeLL(p, NT, mle)
-      resR0[i, j] <- mle
-    }
-
-  J0 <- which.max(resLL)
-  R0hat <- resR0[J0]
-  JJ <- which(resLL == resLL[J0], arr.ind = TRUE)
-  range.max <- ceiling(qgamma(tol, shape = shape[JJ[1]], scale = scale[JJ[2]]))
-  p <- diff(pgamma(0:range.max, shape = shape[JJ[1]], scale = scale[JJ[2]]))
-  p <- p / sum(p)
-
-  return(list(Rhat = R0hat, J0 = J0, ll = resLL, Rs = resR0, scale = scale,
-              shape = shape, JJ = JJ, p = p, range.max = range.max))
-}
-
-#' WP method background function computeLL
-#'
-#' This is a background/internal function called by \code{WP}. It computes the
-#' log-likelihood.
-#'
-#' @param p Discretized version of the serial distribution.
-#' @param NT Vector of case counts.
-#' @param R0 Basic reproductive ratio.
-#'
-#' @return This function returns the log-likelihood at the input variables and
-#'         parameters.
-#'
-#' @noRd
-computeLL <- function(p, NT, R0) {
-  k <- length(p)
-  TT <- length(NT) - 1
-  mu_t <- rep(0, TT)
-
-  for (i in 1:TT) {
-    Nt <- NT[i:max(1, i - k + 1)]
-    mu_t[i] <- sum(p[1:min(k, i)] * Nt)
-  }
-
-  mu_t <- R0 * mu_t
-  LL <- sum(NT[-1] * log(mu_t)) - sum(mu_t)
-
-  return(LL)
-}