Remove 'method' parameter

1 files changed, 30 insertions, 41 deletions

diff --git a/R/WP.R b/R/WP.R
index a0290d3..61f5cd1 100644
--- a/R/WP.R
+++ b/R/WP.R
@@ -9,33 +9,29 @@ source("WP_unknown.R")
 #'
 #' This method is based on a Poisson transmission model, and hence may be most most valid at the beginning
 #' of an epidemic. In their model, the serial distribution is assumed to be discrete with a finite number
-#' of posible values. In this implementation, if the serial distribution is assumed known, it is taken to
+#' of posible values. In this implementation, if \code{mu} is not {NA}, the serial distribution is taken to
 #' be a discretized version of a gamma distribution with mean \code{mu}, shape parameter one, and largest
-#' possible value based on parameter \code{tol}. When the serial distribution is unknown, the function
-#' implements a grid search algorithm to find the maximum likelihood estimator over all possible gamma
-#' distributions with unknown mean and variance, restricting these to a prespecified grid (see
-#' \code{search} parameter).  
+#' possible value based on parameter \code{tol}. When \code{mu} is \code{NA}, the function implements a
+#' grid search algorithm to find the maximum likelihood estimator over all possible gamma distributions
+#' with unknown mean and variance, restricting these to a prespecified grid (see \code{search} parameter).  
 #'
-#' When the serial distribution is taken to be \code{known}, sensitivity testing of the parameter \code{mu}
-#' is strongly recommended. If the serial distribution is \code{unknown}, the likelihood function can be
-#' flat near the maximum, resulting in numerical instability of the optimizer.  When the serial distribution
-#' is \code{unkown} the implementation takes considerably longer to run.  Users should be careful about units
-#' of time (e.g. are counts observed daily or weekly?) when implementing.  
+#' When the serial distribution is known (i.e., \code{mu} is not \code{NA}), sensitivity testing of \code{mu}
+#' is strongly recommended. If the serial distribution is unknown (i.e., \code{mu} is \code{NA}), the
+#' likelihood function can be flat near the maximum, resulting in numerical instability of the optimizer.
+#' When \code{mu} is \code{NA}, the implementation takes considerably longer to run. Users should be careful
+#' about units of time (e.g. are counts observed daily or weekly?) when implementing.  
 #'
 #' The model developed in White and Pagano (2008) is discrete, and hence the serial distribution is finite 
 #' discrete. In our implementation, the input value \code{mu} is that of a continuous distribution. The
-#' algorithm when \code{method="known"} disretizes this input, and hence the mean of the serial distribution
-#' returned in the list \code{SD} will differ from \code{mu} somewhat. That is to say, if the user notices that
-#' the input \code{mu} and out put mean of \code{SD} are different, this is to be expected, and is caused by
-#' the discretization.
+#' algorithm discretizes this input when \code{mu} is not \code{NA}, and hence the mean of the serial
+#' distribution returned in the list \code{SD} will differ from \code{mu} somewhat. That is to say, if the
+#' user notices that the input \code{mu} and output mean of \code{SD} are different, this is to be expected,
+#' and is caused by the discretization.
 #'
 #' @param NT Vector of case counts
 #' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case
 #'           counts are weekly and the serial distribution has a mean of seven days, then \code{mu} should be
 #'           set to one). The default value of \code{mu} is set to \code{NA}.
-#' @param method Variable taking one of two possible values: \code{known} or \code{unknown}. If "known", the
-#'               serial distribution is assumed to be gamma with  rate 1/\code{mu} and shape equal to one, if
-#'               "unknown" then the serial distribution is gamma with unknown parameters. Defaults to "unknown"
 #' @param search List of default values for the grid search algorithm; the list includes three elements: the
 #'               first is \code{B} which is the length of the grid in one dimension, the second is
 #'               \code{scale.max} which is the largest possible value of the scale parameter, and the third is
@@ -47,8 +43,8 @@ source("WP_unknown.R")
 #'            original gamma distribution has cumulative probability of no more than 0.999 at this maximum).
 #'
 #' @return WP returns a list containing the following components:  \code{Rhat} is the estimate of R0, \code{SD}
-#'            is either the discretized serial distribution (if \code{method="known"}) or the estimated
-#'            discretized serial distribution (if \code{method="unknown"}), and \code{inputs} is a list of the
+#'            is either the discretized serial distribution (if \code{mu} is not \code{NA}) or the estimated
+#'            discretized serial distribution (if \code{mu} is \code{NA}), and \code{inputs} is a list of the
 #'            original input variables \code{NT, mu, method, search, tol}. The list also returns the variable
 #'            \code{check}, which is equal to the number of non-unique maximum likelihood estimators. The serial
 #'            distribution \code{SD} is returned as a list made up of \code{supp} the support of the distribution
@@ -59,16 +55,16 @@ source("WP_unknown.R")
 #' ## Illustrate on weekly data                             ##
 #' ## ===================================================== ##
 #'
-#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)	
+#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
 #' ## obtain Rhat when serial distribution has mean of five days
-#' res1 <- WP(NT=NT, mu=5/7, method="known")	
+#' res1 <- WP(NT=NT, mu=5/7)
 #' res1$Rhat
 #' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- WP(NT=NT, mu=3/7, method="known")	
+#' res2	<- WP(NT=NT, mu=3/7)
 #' res2$Rhat
 #' ## obtain Rhat when serial distribution is unknown
 #' ## NOTE:  this implementation will take longer to run
-#' res3	<- WP(NT=NT)	
+#' res3	<- WP(NT=NT)
 #' res3$Rhat
 #' ## find mean of estimated serial distribution
 #' serial <- res3$SD
@@ -78,33 +74,26 @@ source("WP_unknown.R")
 #' ## Compute Rhat using only the first five weeks of data      ##
 #' ## ========================================================= ##
 #' 
-#' res4 <- WP(NT=NT[1:5], mu=5/7, method="known") # serial distribution has mean of five days
+#' res4 <- WP(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
 #' res4$Rhat
 #'
 #' @export
-WP <- function(NT, mu="NA", method="unknown", search=list(B=100, shape.max=10, scale.max=10), tol=0.999) {
-    if (method == "unknown") {
+WP <- function(NT, mu=NA, search=list(B=100, shape.max=10, scale.max=10), tol=0.999) {
+    if (is.na(mu)) {
         print("You have assumed that the serial distribution is unknown.")
         res <- WP_unknown(NT=NT, B=search$B, shape.max=search$shape.max, scale.max=search$scale.max, tol=tol)
         Rhat <- res$Rhat
         p <- res$p
         range.max <- res$range.max
         JJ <- res$JJ
-    }
-
-	if (method == "known") {
-        if (mu=="NA") {
-            res <- "NA"
-            print("For method=known, the mean of the serial distribution must be specified.")
-        } else {
-            print("You have assumed that the serial distribution is known.")
-            range.max <- ceiling(qexp(tol, rate=1/mu))
-            p <- diff(pexp(0:range.max, 1/mu))
-            p <- p / sum(p)
-            res <- WP_known(NT=NT, p=p)
-            Rhat <- res
-            JJ <- NA
-        }
+    } else {
+        print("You have assumed that the serial distribution is known.")
+        range.max <- ceiling(qexp(tol, rate=1/mu))
+        p <- diff(pexp(0:range.max, 1/mu))
+        p <- p / sum(p)
+        res <- WP_known(NT=NT, p=p)
+        Rhat <- res
+        JJ <- NA
     }
 
     return(list(Rhat=Rhat, check=length(JJ), SD=list(supp=1:range.max, pmf=p)))