X-Git-Url: https://git.nmode.ca/Rnaught/blobdiff_plain/14ec98c9bfc49f963e30159dc7db9f554088fb44..80624375941bebfd3629f3972555e5a63ba66ec1:/R/WP_unknown.R

diff --git a/R/WP_unknown.R b/R/WP_unknown.R
index a450b54..f339836 100644
--- a/R/WP_unknown.R
+++ b/R/WP_unknown.R
@@ -1,47 +1,54 @@
 #' 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. 
+#' @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).
 #'
-#' @export
+#' @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]
 
-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)
+            resR0[i,j] <- mle
+        }
+    }
 	
-	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))
 }
-
-