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[Rnaught] / R / seqB.R

diff --git a/R/seqB.R b/R/seqB.R
index 7938311f2f853179d607461ba05f0616a947e175..a64b598a4d653535a49c6c17a47f7dbacfedc9a8 100644 (file)
--- a/R/seqB.R
+++ b/R/seqB.R
@@ -7,43 +7,42 @@
 #' The distribution of R0 is then updated sequentially, with one update for each new case count observation.
 #' The final estimate of R0 is \code{Rhat}, the mean of the (last) posterior distribution.
 #' The prior distribution is the initial belief of the distribution of R0; which in this implementation is the uninformative uniform
 #' The distribution of R0 is then updated sequentially, with one update for each new case count observation.
 #' The final estimate of R0 is \code{Rhat}, the mean of the (last) posterior distribution.
 #' The prior distribution is the initial belief of the distribution of R0; which in this implementation is the uninformative uniform

-#' distribution with values between zero and \code{kappa}. Users can change the value of kappa only (ie. the prior distribution
+#' distribution with values between zero and \code{kappa}. Users can change the value of /code{kappa} only (i.e., the prior distribution

 #' cannot be changed from the uniform).  As more case counts are observed, the influence of the prior distribution should lessen on
 #' the final estimate \code{Rhat}.
 #'
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic. The method assumes
 #' that the mean of the serial distribution (sometimes called the serial interval) is known. The final estimate can be quite sensitive
 #' cannot be changed from the uniform).  As more case counts are observed, the influence of the prior distribution should lessen on
 #' the final estimate \code{Rhat}.
 #'
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic. The method assumes
 #' that the mean of the serial distribution (sometimes called the serial interval) is known. The final estimate can be quite sensitive

-#' to this value, so sensitivity testing is strongly recommended. Users should be careful about units of time (e.g. are counts observed
+#' to this value, so sensitivity testing is strongly recommended. Users should be careful about units of time (e.g., are counts observed

 #' daily or weekly?) when implementing.  
 #'
 #' Our code has been modified to provide an estimate even if case counts equal to zero are present in some time intervals. This is done
 #' by grouping the counts over such periods of time. Without grouping, and in the presence of zero counts, no estimate can be provided.
 #'
 #' daily or weekly?) when implementing.  
 #'
 #' Our code has been modified to provide an estimate even if case counts equal to zero are present in some time intervals. This is done
 #' by grouping the counts over such periods of time. Without grouping, and in the presence of zero counts, no estimate can be provided.
 #'

-#' @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, if case
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)
-#' @param kappa Largest possible value of uniform prior, defaults to 20. This describes the prior belief on ranges of R0,
-#'              so should be set to a higher value if R0 is believed to be larger.  
+#' @param NT Vector of case counts.
+#' @param mu Mean of the serial distribution. This 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. If case
+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.
+#' @param kappa Largest possible value of uniform prior (defaults to 20). This describes the prior belief on ranges of R0,
+#'              and should be set to a higher value if R0 is believed to be larger.  

 #'
 #'

-#' @return secB returns a list containing the following components: \code{Rhat} is the estimate of R0 (the posterior mean),
-#'              \code{posterior} is the posterior distribution of R0 from which alternate estimates can be obtained (see examples),
-#'              \code{group} is an indicator variable (if \code{group=TRUE}, zero values of NT were input and grouping was done to
-#'              obtain \code{Rhat}), and \code{inputs} is a list of the original input variables \code{NT, gamma, kappa}. The variable
-#'              \code{posterior} is returned as a list made up of \code{supp} the support of the distribution and \code{pmf} the
-#'              probability mass function.
+#' @return \code{secB} returns a list containing the following components: \code{Rhat} is the estimate of R0 (the posterior mean),
+#'         \code{posterior} is the posterior distribution of R0 from which alternate estimates can be obtained (see examples),
+#'         and \code{group} is an indicator variable (if \code{group=TRUE}, zero values of NT were input and grouping was done 
+#'         to obtain \code{Rhat}). The variable \code{posterior} 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                             ##
 #' ## ===================================================== ##
 #'
 #'
 #' @examples
 #' ## ===================================================== ##
 #' ## 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
 #' ## obtain Rhat when serial distribution has mean of five days

-#' res1 <- seqB(NT=NT, mu=5/7) 
+#' res1 <- seqB(NT=NT, mu=5/7)

 #' res1$Rhat
 #' ## obtain Rhat when serial distribution has mean of three days
 #' res1$Rhat
 #' ## obtain Rhat when serial distribution has mean of three days

-#' res2        <- seqB(NT=NT, mu=3/7)  
+#' res2        <- seqB(NT=NT, mu=3/7)

 #' res2$Rhat
 #'
 #' ## ============================================================= ##
 #' res2$Rhat
 #'
 #' ## ============================================================= ##