Re-style code and enforce 80 character line limit

1 files changed, 89 insertions, 75 deletions

diff --git a/R/WP.R b/R/WP.R
index c8178b7..a6e5354 100644
--- a/R/WP.R
+++ b/R/WP.R
@@ -1,98 +1,112 @@
 #' WP method
 #'
-#' This function implements an R0 estimation due to White and Pagano (Statistics in Medicine, 2008).
-#' The method is based on maximum likelihood estimation in a Poisson transmission model.
-#' See details for important implementation notes.
+#' This function implements an R0 estimation due to White and Pagano (Statistics
+#' in Medicine, 2008). The method is based on maximum likelihood estimation in a
+#' Poisson transmission model. See details for important implementation notes.
 #'
-#' 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 \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 \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).  
+#' 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 \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 \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 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.  
+#' 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 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,
+#' 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
+#' 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 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 \code{shape.max}, which is the largest possible value of the shape parameter. Defaults to
-#'               \code{B=100, scale.max=10, shape.max=10}. For both shape and scale, the smallest possible
-#'               value is 1/\code{B}.
-#' @param tol Cutoff value for cumulative distribution function of the pre-discretization gamma serial
-#'            distribution. Defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the
-#'            original gamma distribution has cumulative probability of no more than 0.999 at this maximum).
+#' @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 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 \code{shape.max}, which is
+#'               the largest possible value of the shape parameter. Defaults to
+#'               \code{B=100, scale.max=10, shape.max=10}. For both shape and
+#'               scale, the smallest possible value is 1/\code{B}.
+#' @param tol Cutoff value for cumulative distribution function of the
+#'            pre-discretization gamma serial distribution. Defaults to 0.999
+#'            (i.e. in the discretization, the maximum is chosen such that the
+#'            original gamma distribution has cumulative probability of no more
+#'            than 0.999 at this maximum).
 #'
-#' @return \code{WP} returns a list containing the following components:  \code{Rhat} is the estimate of R0,
-#'         and \code{SD} 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}). 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) and \code{pmf} (the probability mass function).
+#' @return \code{WP} returns a list containing the following components:
+#'         \code{Rhat} is the estimate of R0, and \code{SD} 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}). 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) and \code{pmf} (the
+#'         probability mass function).
 #'
 #' @examples
-#' ## ===================================================== ##
-#' ## Illustrate on weekly data                             ##
-#' ## ===================================================== ##
-#'
+#' # Weekly data.
 #' 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)
+#'
+#' # Obtain R0 when the serial distribution has a mean of five days.
+#' res1 <- WP(NT, mu = 5 / 7)
 #' res1$Rhat
-#' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- WP(NT=NT, mu=3/7)
+#'
+#' # Obtain R0 when the serial distribution has a mean of three days.
+#' res2 <- WP(NT, mu = 3 / 7)
 #' res2$Rhat
-#' ## obtain Rhat when serial distribution is unknown
-#' ## NOTE:  this implementation will take longer to run
-#' res3	<- WP(NT=NT)
+#'
+#' # Obtain R0 when the serial distribution is unknown.
+#' # NOTE: This implementation will take longer to run.
+#' res3 <- WP(NT)
 #' res3$Rhat
-#' ## find mean of estimated serial distribution
+#'
+#' # Find the mean of the estimated serial distribution.
 #' serial <- res3$SD
 #' sum(serial$supp * serial$pmf)
 #'
-#' ## ========================================================= ##
-#' ## Compute Rhat using only the first five weeks of data      ##
-#' ## ========================================================= ##
-#' 
-#' res4 <- WP(NT=NT[1:5], mu=5/7) # serial distribution has mean of five days
-#' res4$Rhat
-#'
 #' @importFrom stats pexp qexp
 #'
 #' @export
-WP <- function(NT, mu=NA, search=list(B=100, shape.max=10, scale.max=10), tol=0.999) {
-    if (is.na(mu)) {
-        print("You have assumed that the serial distribution is unknown.")
-        res <- WP_unknown(NT=NT, B=search$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
-    } 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
-    }
+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, 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
+  } 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)))
+  return(list(Rhat = Rhat,
+              check = length(JJ),
+              SD = list(supp = 1:range.max, pmf = p)))
 }