Re-style code and enforce 80 character line limit

[Rnaught] / R / WP_internal.R

diff --git a/R/WP_internal.R b/R/WP_internal.R
index 54744b9a3012bce2682f6705be073b8a2cf6fef1..420d0c05b2a5479448ef235561c469131ec07354 100644 (file)
--- a/R/WP_internal.R
+++ b/R/WP_internal.R
@@ -1,7 +1,8 @@
 #' WP method background function WP_known
 #'
 #' WP method background function WP_known
 #'

-#' This is a background/internal function called by \code{WP}. It computes the maximum
-#' likelihood estimator of R0 assuming that the serial distribution is known and finite discrete.
+#' This is a background/internal function called by \code{WP}. It computes the
+#' maximum likelihood estimator of R0 assuming that the serial distribution is
+#' known and finite discrete.

 #'
 #' @param NT Vector of case counts.
 #' @param p Discretized version of the serial distribution.
 #'
 #' @param NT Vector of case counts.
 #' @param p Discretized version of the serial distribution.


@@ -10,97 +11,106 @@
 #'
 #' @keywords internal
 WP_known <- function(NT, p) {
 #'
 #' @keywords internal
 WP_known <- function(NT, p) {

-    k <- length(p)
-    TT <- length(NT) - 1
-       mu_t <- rep(0, TT)
+  k <- length(p)
+  TT <- length(NT) - 1
+  mu_t <- rep(0, TT)

 
 

-    for (i in 1:TT) {
-        Nt <- NT[i:max(1, i-k+1)]
-        mu_t[i]        <- sum(p[1:min(k, i)] * Nt)
-    }
+  for (i in 1:TT) {
+    Nt <- NT[i:max(1, i - k + 1)]
+    mu_t[i] <- sum(p[1:min(k, i)] * Nt)
+  }

 
 

-    Rhat <- sum(NT[-1]) / sum(mu_t)
-       return(Rhat)
+  Rhat <- sum(NT[-1]) / sum(mu_t)
+  return(Rhat)

 }
 
 #' WP method background function WP_unknown
 #'
 }
 
 #' WP method background function WP_unknown
 #'

-#' This is a background/internal function called by \code{WP}. It computes the maximum likelihood estimator
-#' of R0 assuming that the serial distribution is unknown but comes from a discretized gamma distribution.
-#' The function then implements a simple grid search algorithm to obtain the maximum likelihood estimator
-#' of R0 as well as the gamma parameters.
+#' This is a background/internal function called by \code{WP}. It computes the
+#' maximum likelihood estimator of R0 assuming that the serial distribution is
+#' unknown but comes from a discretized gamma distribution. The function then
+#' implements a simple grid search algorithm to obtain the maximum likelihood
+#' estimator of R0 as well as the gamma parameters.

 #'
 #' @param NT Vector of case counts.
 #' @param B Length of grid for shape and scale (grid search parameter).
 #' @param shape.max Maximum shape value (grid \code{search} parameter).
 #' @param scale.max Maximum scale value (grid \code{search} parameter).
 #'
 #' @param NT Vector of case counts.
 #' @param B Length of grid for shape and scale (grid search parameter).
 #' @param shape.max Maximum shape value (grid \code{search} parameter).
 #' @param scale.max Maximum scale value (grid \code{search} parameter).

-#' @param tol cutoff value for cumulative distribution function of the serial distribution (defaults to 0.999).
+#' @param tol cutoff value for cumulative distribution function of the serial
+#'            distribution (defaults to 0.999).

 #'
 #'

-#' @return The function returns \code{Rhat}, the maximum likelihood estimator of R0, as well as the maximum
-#'         likelihood estimator of the discretized serial distribution given by \code{p} (the probability mass
-#'         function) and \code{range.max} (the distribution has support on the integers one to \code{range.max}).
-#'         The function also returns \code{resLL} (all values of the log-likelihood) at \code{shape} (grid for
-#'         shape parameter) and at \code{scale} (grid for scale parameter), as well as \code{resR0} (the full
-#'         vector of maximum likelihood estimators), \code{JJ} (the locations for the likelihood for these), and
-#'         \code{J0} (the location for the maximum likelihood estimator \code{Rhat}). If \code{JJ} and \code{J0}
-#'         are not the same, this means that the maximum likelihood estimator is not unique.
+#' @return The function returns \code{Rhat}, the maximum likelihood estimator of
+#'         R0, as well as the maximum likelihood estimator of the discretized
+#'         serial distribution given by \code{p} (the probability mass function)
+#'         and \code{range.max} (the distribution has support on the integers
+#'         one to \code{range.max}). The function also returns \code{resLL} (all
+#'         values of the log-likelihood) at \code{shape} (grid for shape
+#'         parameter) and at \code{scale} (grid for scale parameter), as well as
+#'         \code{resR0} (the full vector of maximum likelihood estimators),
+#'         \code{JJ} (the locations for the likelihood for these), and \code{J0}
+#'         (the location for the maximum likelihood estimator \code{Rhat}). If
+#'         \code{JJ} and \code{J0} are not the same, this means that the maximum
+#'         likelihood estimator is not unique.

 #'
 #' @importFrom stats pgamma qgamma
 #'
 #' @keywords internal
 #'
 #' @importFrom stats pgamma qgamma
 #'
 #' @keywords internal

-WP_unknown <- function(NT, B=100, shape.max=10, scale.max=10, tol=0.999) {
-       shape <- seq(0, shape.max, length.out=B+1)
-       scale <- seq(0, scale.max, length.out=B+1)
-       shape <- shape[-1]
-       scale <- scale[-1]
+WP_unknown <- function(NT, B = 100, shape.max = 10, scale.max = 10,
+                       tol = 0.999) {
+  shape <- seq(0, shape.max, length.out = B + 1)
+  scale <- seq(0, scale.max, length.out = B + 1)
+  shape <- shape[-1]
+  scale <- scale[-1]

 
 

-       resLL <- matrix(0,B,B)
-       resR0 <- matrix(0,B,B)
+  resLL <- matrix(0, B, B)
+  resR0 <- matrix(0, B, B)

 
 

-    for (i in 1:B) {
-        for (j in 1:B) {
-            range.max <- ceiling(qgamma(tol, shape=shape[i], scale=scale[j]))
-            p <- diff(pgamma(0:range.max, shape=shape[i], scale=scale[j]))
-            p <- p / sum(p)
-            mle <- WP_known(NT, p)
-            resLL[i,j] <- computeLL(p, NT, mle)
-            resR0[i,j] <- mle
-        }
+  for (i in 1:B)
+    for (j in 1:B) {
+      range.max <- ceiling(qgamma(tol, shape = shape[i], scale = scale[j]))
+      p <- diff(pgamma(0:range.max, shape = shape[i], scale = scale[j]))
+      p <- p / sum(p)
+      mle <- WP_known(NT, p)
+      resLL[i, j] <- computeLL(p, NT, mle)
+      resR0[i, j] <- mle

     }
     }

-       
-    J0 <- which.max(resLL)
-    R0hat <- resR0[J0]
-    JJ <- which(resLL == resLL[J0], arr.ind=TRUE)
-    range.max <- ceiling(qgamma(tol, shape=shape[JJ[1]], scale=scale[JJ[2]]))
-    p <- diff(pgamma(0:range.max, shape=shape[JJ[1]], scale=scale[JJ[2]]))
-    p <- p / sum(p)
-       
-    return(list(Rhat=R0hat, J0=J0, ll=resLL, Rs=resR0, scale=scale, shape=shape, JJ=JJ, p=p, range.max=range.max))
+
+  J0 <- which.max(resLL)
+  R0hat <- resR0[J0]
+  JJ <- which(resLL == resLL[J0], arr.ind = TRUE)
+  range.max <- ceiling(qgamma(tol, shape = shape[JJ[1]], scale = scale[JJ[2]]))
+  p <- diff(pgamma(0:range.max, shape = shape[JJ[1]], scale = scale[JJ[2]]))
+  p <- p / sum(p)
+
+  return(list(Rhat = R0hat, J0 = J0, ll = resLL, Rs = resR0, scale = scale,
+              shape = shape, JJ = JJ, p = p, range.max = range.max))

 }
 
 #' WP method background function computeLL
 #'
 }
 
 #' WP method background function computeLL
 #'

-#' This is a background/internal function called by \code{WP}.  It computes the log-likelihood.
+#' This is a background/internal function called by \code{WP}. It computes the
+#' log-likelihood.

 #'
 #' @param NT Vector of case counts.
 #' @param p Discretized version of the serial distribution.
 #' @param R0 Basic reproductive ratio.
 #'
 #'
 #' @param NT Vector of case counts.
 #' @param p Discretized version of the serial distribution.
 #' @param R0 Basic reproductive ratio.
 #'

-#' @return This function returns the log-likelihood at the input variables and parameters.
+#' @return This function returns the log-likelihood at the input variables and
+#'         parameters.

 #'
 #' @keywords internal
 computeLL <- function(p, NT, R0) {
 #'
 #' @keywords internal
 computeLL <- function(p, NT, R0) {

-    k <- length(p)
-    TT <- length(NT) - 1
-    mu_t <- rep(0, TT)
+  k <- length(p)
+  TT <- length(NT) - 1
+  mu_t <- rep(0, TT)

 
 

-    for (i in 1:TT) {
-        Nt <- NT[i:max(1, i-k+1)]
-        mu_t[i]        <- sum(p[1:min(k, i)] * Nt)
-    }
+  for (i in 1:TT) {
+    Nt <- NT[i:max(1, i - k + 1)]
+    mu_t[i] <- sum(p[1:min(k, i)] * Nt)
+  }

 
 

-    mu_t <- R0 * mu_t
-    LL <- sum(NT[-1] * log(mu_t)) - sum(mu_t)
+  mu_t <- R0 * mu_t
+  LL <- sum(NT[-1] * log(mu_t)) - sum(mu_t)

 
 

-       return(LL)
+  return(LL)

 }
 }