Group internal functions for WP method

diff --git a/R/WP_known.R b/R/WP_known.R
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--- a/R/WP_known.R
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-#' 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 (file)
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--- a/R/WP_unknown.R
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-#' 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
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--- a/R/computeLL.R
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-#' 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)
-}