diff options
| -rw-r--r-- | R/WP.R | 117 | ||||
| -rw-r--r-- | R/WP_internal.R | 116 |
2 files changed, 117 insertions, 116 deletions
@@ -110,3 +110,120 @@ WP <- function(NT, mu = NA, check = length(JJ), SD = list(supp = 1:range.max, pmf = p))) } + +#' 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) +} diff --git a/R/WP_internal.R b/R/WP_internal.R deleted file mode 100644 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) -} |