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authorNaeem Model <me@nmode.ca>2023-06-29 21:37:15 +0000
committerNaeem Model <me@nmode.ca>2023-06-29 21:37:15 +0000
commitff1d5fa81a10cc374aa40fa13c697baa6ade136c (patch)
tree79ca60a8f5af3f9a09adcede670ba9c37a17eb41
parent18358add05e58e466ba7098f771e20d8d0599d0e (diff)
Group internal functions for WP method
-rw-r--r--R/WP_known.R24
-rw-r--r--R/WP_unknown.R54
-rw-r--r--R/computeLL.R26
3 files changed, 0 insertions, 104 deletions
diff --git a/R/WP_known.R b/R/WP_known.R
deleted file mode 100644
index 4c43ed2..0000000
--- a/R/WP_known.R
+++ /dev/null
@@ -1,24 +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.
-#'
-#' @keywords internal
-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)
-}
diff --git a/R/WP_unknown.R b/R/WP_unknown.R
deleted file mode 100644
index f339836..0000000
--- a/R/WP_unknown.R
+++ /dev/null
@@ -1,54 +0,0 @@
-#' 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
-#'
-#' @keywords internal
-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))
-}
diff --git a/R/computeLL.R b/R/computeLL.R
deleted file mode 100644
index 01c0d57..0000000
--- a/R/computeLL.R
+++ /dev/null
@@ -1,26 +0,0 @@
-#' WP method background function computeLL
-#'
-#' This is a background/internal function called by \code{WP}. It computes the log-likelihood.
-#'
-#' @param NT Vector of case counts.
-#' @param p Discretized version of the serial distribution.
-#' @param R0 Basic reproductive ratio.
-#'
-#' @return This function returns the log-likelihood at the input variables and parameters.
-#'
-#' @keywords internal
-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)
-}