Fix code formatting and remove unnecessary comments

[Rnaught] / R / WP_unknown.R

diff --git a/R/WP_unknown.R b/R/WP_unknown.R
index a450b54a599119f56a26f85020405c31d2a3dab1..c5b0a356483b6369d37cd3bcbd77889cc5dd3886 100644 (file)
--- 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))
 }
-
-