#' 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
#'
#' ## ============================================================= ##
#' ## ============================================================= ##
#'
#' 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
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))
}
}
git.nmode.ca / Rnaught / blobdiff
