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Diffstat (limited to 'R/WP_unknown.R')
| -rw-r--r-- | R/WP_unknown.R | 71 |
1 files changed, 38 insertions, 33 deletions
diff --git a/R/WP_unknown.R b/R/WP_unknown.R index a450b54..c5b0a35 100644 --- a/R/WP_unknown.R +++ b/R/WP_unknown.R @@ -1,47 +1,52 @@ #' 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. +#' 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 search parameter) #' @param scale.max maximum scale value (grid 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. +#' +#' @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. #' #' @export +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] -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$R) - resR0[i,j] <- mle$R - } -# print(i) - } + 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$R) + resR0[i,j] <- mle$R + } + } - J0 <- which.max(resLL) - R0hat <- resR0[J0] - JJ <- which(resLL==resLL[J0], arr.ind=TRUE) -# JJ <- which(resLL==max(resLL), 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) + 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)) + return(list(Rhat=R0hat, J0=J0, ll=resLL, Rs=resR0, scale=scale, shape=shape, JJ=JJ, p=p, range.max=range.max)) } - - |