X-Git-Url: https://git.nmode.ca/Rnaught/blobdiff_plain/9c1a5668803e735f034700c55028ffc0146f1e93..a50ca5855eecf12908327252d627df3af076fc88:/R/seqB.R?ds=inline

diff --git a/R/seqB.R b/R/seqB.R
index 0f8a1b9..8685f39 100644
--- 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
-#' 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
-#' 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.
 #'
-#' @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                             ##
 #' ## ===================================================== ##
 #'
-#' 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 <- seqB(NT=NT, mu=5/7)	
+#' res1 <- seqB(NT=NT, mu=5/7)
 #' res1$Rhat
 #' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- seqB(NT=NT, mu=3/7)	
+#' res2	<- seqB(NT=NT, mu=3/7)
 #' res2$Rhat
 #'
 #' ## ============================================================= ##
@@ -51,26 +50,27 @@
 #' ## ============================================================= ##
 #'
 #' Rpost <-	res1$posterior
-#' loc <- which(Rpost$pmf==max(Rpost$pmf))
-#' Rpost$supp[loc]		# posterior mode
-#' res1$Rhat		# compare with posterior mean
+#' loc <- which(Rpost$pmf == max(Rpost$pmf))
+#' Rpost$supp[loc] # posterior mode
+#' res1$Rhat # compare with posterior mean
 #'
-#' par(mfrow=c(2,1), mar=c(2,2,1,1))
+#' par(mfrow=c(2, 1), mar=c(2, 2, 1, 1))
 #' plot(Rpost$supp, Rpost$pmf, col="black", type="l", xlab="", ylab="")
 #' abline(h=1/(20/0.01+1), col="red")
 #' abline(v=res1$Rhat, col="blue")
 #' abline(v=Rpost$supp[loc], col="purple")
-#' legend("topright", legend=c("prior", "posterior", "posterior mean (Rhat)", "posterior mode"), col=c("red", "black", "blue", "purple"), lty=1)
-#' plot(Rpost$supp, Rpost$pmf, col="black", type="l", xlim=c(0.5,1.5), xlab="", ylab="")
+#' legend("topright", legend=c("prior", "posterior", "posterior mean (Rhat)", "posterior mode"),
+#'        col=c("red", "black", "blue", "purple"), lty=1)
+#' plot(Rpost$supp, Rpost$pmf, col="black", type="l", xlim=c(0.5, 1.5), xlab="", ylab="")
 #' abline(h=1/(20/0.01+1), col="red")
 #' abline(v=res1$Rhat, col="blue")
 #' abline(v=Rpost$supp[loc], col="purple")
-#' legend("topright", legend=c("prior", "posterior", "posterior mean (Rhat)", "posterior mode"), col=c("red", "black", "blue", "purple"), lty=1)
+#' legend("topright", legend=c("prior", "posterior", "posterior mean (Rhat)", "posterior mode"),
+#'        col=c("red", "black", "blue", "purple"), lty=1)
 #'
 #' ## ========================================================= ##
 #' ## Compute Rhat using only the first five weeks of data      ##
 #' ## ========================================================= ##
-#'
 #' 
 #' res3 <- seqB(NT=NT[1:5], mu=5/7)	# serial distribution has mean of five days
 #' res3$Rhat
@@ -121,6 +121,6 @@ seqB <- function(NT, mu, kappa=20) {
 
         Rhat <- sum(R * posterior)
 
-        return(list(Rhat=Rhat, posterior=list(supp=R, pmf=posterior), group=group, inputs=list(NT=NT, mu=mu, kappa=kappa)))
+        return(list(Rhat=Rhat, posterior=list(supp=R, pmf=posterior), group=group))
     }	
 }