diff options
| -rw-r--r-- | R/WP_known.R | 24 | ||||
| -rw-r--r-- | R/WP_unknown.R | 54 | ||||
| -rw-r--r-- | R/computeLL.R | 26 |
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) -} |