Re-style code and enforce 80 character line limit

1 files changed, 102 insertions, 99 deletions

diff --git a/R/seqB.R b/R/seqB.R
index 8685f39..e51117f 100644
--- a/R/seqB.R
+++ b/R/seqB.R
@@ -1,126 +1,129 @@
 #' seqB method
 #'
-#' This function implements a sequential Bayesian estimation method of R0 due to Bettencourt and Riberio (PloS One, 2008).
-#' See details for important implementation notes.
+#' This function implements a sequential Bayesian estimation method of R0 due to
+#' Bettencourt and Riberio (PloS One, 2008). See details for important
+#' implementation notes.
 #'
-#' The method sets a uniform prior distribution on R0 with possible values between zero and \code{kappa}, discretized to a fine grid.
-#' 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 /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}.
+#' The method sets a uniform prior distribution on R0 with possible values
+#' between zero and \code{kappa}, discretized to a fine grid. 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 is the uninformative uniform 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
-#' daily or weekly?) when implementing.  
+#' 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 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.
+#' 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. 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.  
+#' @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 \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).
+#' @return \code{seqB} 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                             ##
-#' ## ===================================================== ##
-#'
+#' # 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 <- seqB(NT=NT, mu=5/7)
+#'
+#' ## Obtain R0 when the serial distribution has a mean of five days.
+#' res1 <- seqB(NT, mu = 5 / 7)
 #' res1$Rhat
-#' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- seqB(NT=NT, mu=3/7)
+#'
+#' ## Obtain R0 when the serial distribution has a mean of three days.
+#' res2 <- seqB(NT, mu = 3 / 7)
 #' res2$Rhat
 #'
-#' ## ============================================================= ##
-#' ## Compute posterior mode instead of posterior mean and plot     ##
-#' ## ============================================================= ##
+#' # Compute posterior mode instead of posterior mean and plot.
 #'
-#' Rpost <-	res1$posterior
+#' Rpost <- res1$posterior
 #' loc <- which(Rpost$pmf == max(Rpost$pmf))
-#' Rpost$supp[loc] # posterior mode
-#' res1$Rhat # compare with posterior mean
+#' Rpost$supp[loc] # Posterior mode.
+#' res1$Rhat # Compare with the posterior mean.
 #'
-#' 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="")
-#' 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)
+#' par(mfrow = c(2, 1), mar = c(2, 2, 1, 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$Rhat
+#' plot(Rpost$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", "Posterior mode"),
+#'   col = c("red", "black", "blue", "purple"), lty = 1)
 #'
 #' @export
-seqB <- function(NT, mu, kappa=20) {	
-    if (length(NT) < 2)
-        print("Warning: length of NT should be at least two.")
-    else {
-        if (min(NT) > 0) {
-            times <- 1:length(NT)
-            tau <- diff(times)
-        }
-	    group <- FALSE
-        if (min(NT) == 0) {
-            times <- which(NT > 0)
-            NT <- NT[times]
-            tau <- diff(times)
-            group <- TRUE
-        }
+seqB <- function(NT, mu, kappa = 20) {
+  if (length(NT) < 2) {
+    print("Warning: length of NT should be at least two.")
+  } else {
+    if (min(NT) > 0) {
+      times <- 1:length(NT)
+      tau <- diff(times)
+    }
+    group <- FALSE
+    if (min(NT) == 0) {
+      times <- which(NT > 0)
+      NT <- NT[times]
+      tau <- diff(times)
+      group <- TRUE
+    }
 
-        R <- seq(0, kappa, 0.01)
-        prior0 <- rep(1, kappa / 0.01 + 1)
-        prior0 <- prior0 / sum(prior0)
-        k <- length(NT) - 1
-        R0.post <- matrix(0, nrow=k, ncol=length(R))
-        prior <- prior0
-        posterior <- seq(0, length(prior0))
-        gamma <- 1 / mu
+    R <- seq(0, kappa, 0.01)
+    prior0 <- rep(1, kappa / 0.01 + 1)
+    prior0 <- prior0 / sum(prior0)
+    k <- length(NT) - 1
+    R0.post <- matrix(0, nrow = k, ncol = length(R))
+    prior <- prior0
+    posterior <- seq(0, length(prior0))
+    gamma <- 1 / mu
 
-        for (i in 1:k) {
-            mm1 <- NT[i]
-            mm2 <- NT[i+1]
-            lambda <- tau[i] * gamma * (R - 1)
-            lambda <- log(mm1) + lambda
-            loglik <- mm2 * lambda - exp(lambda)
-            maxll <- max(loglik)
-            const <- 0
+    for (i in 1:k) {
+      mm1 <- NT[i]
+      mm2 <- NT[i + 1]
+      lambda <- tau[i] * gamma * (R - 1)
+      lambda <- log(mm1) + lambda
+      loglik <- mm2 * lambda - exp(lambda)
+      maxll <- max(loglik)
+      const <- 0
 
-            if (maxll > 700)
-                const <- maxll - 700
+      if (maxll > 700)
+        const <- maxll - 700
 
-            loglik <- loglik-const
-            posterior <- exp(loglik) * prior
-            posterior <- posterior / sum(posterior)
-            prior <- posterior
-        }
+      loglik <- loglik - const
+      posterior <- exp(loglik) * prior
+      posterior <- posterior / sum(posterior)
+      prior <- posterior
+    }
 
-        Rhat <- sum(R * posterior)
+    Rhat <- sum(R * posterior)
 
-        return(list(Rhat=Rhat, posterior=list(supp=R, pmf=posterior), group=group))
-    }	
+    return(list(Rhat = Rhat,
+                posterior = list(supp = R, pmf = posterior),
+                group = group))
+  }
 }