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diff --git a/R/ID.R b/R/ID.R
index b5fc8c2e78c7139aa4e696510bc3bf34e1921400..cd991a43ce6e07a93be86a7833839930c22a8b24 100644 (file)
--- a/R/ID.R
+++ b/R/ID.R
@@ -9,15 +9,14 @@
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.\r
 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.\r
 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.\r
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.\r
 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.\r
 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.\r

-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.\r
+#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.\r

 #'\r
 #'\r

-#' @param NT Vector of case counts\r
-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are\r
-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case\r
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)\r
+#' @param NT Vector of case counts.\r
+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts\r
+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one If case\r
+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.\r

 #'\r
 #'\r

-#' @return \code{ID} returns a list containing the following components:  \code{Rhat} is the estimate of R0 and\r
-#'         \code{inputs} is a list of the original input variables \code{NT, mu}.\r
+#' @return \code{ID} returns a single value, the estimate of R0.\r

 #'\r
 #' @examples\r
 #' ## ===================================================== ##\r
 #'\r
 #' @examples\r
 #' ## ===================================================== ##\r

diff --git a/R/IDEA.R b/R/IDEA.R
index 53cfaa7626b2fc31d18052faaf909d1947aead3b..20a94016707747120b5e795ca6bd19af00db6b4f 100644 (file)
--- a/R/IDEA.R
+++ b/R/IDEA.R
@@ -10,15 +10,14 @@
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.\r
 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.\r
 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.\r
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.\r
 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.\r
 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.\r

-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.\r
+#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.\r

 #'\r
 #'\r

-#' @param NT Vector of case counts\r
-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are\r
-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case\r
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)\r
+#' @param NT Vector of case counts.\r
+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts\r
+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one. If case\r
+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.\r

 #'\r
 #'\r

-#' @return \code{IDEA} returns a list containing the following components: \code{Rhat} is the estimate of R0 and\r
-#'         \code{inputs} is a list of the original input variables \code{NT, mu}.\r
+#' @return \code{IDEA} returns a single value, the estimate of R0.\r

 #'\r
 #' @examples\r
 #' ## ===================================================== ##\r
 #'\r
 #' @examples\r
 #' ## ===================================================== ##\r

diff --git a/R/WP.R b/R/WP.R
index 61f5cd164b1d1c2eb601d777ff80790f4a831051..7597c4993d48254cfa0b2065ff85199761dbff95 100644 (file)
--- a/R/WP.R
+++ b/R/WP.R
@@ -19,7 +19,7 @@ source("WP_unknown.R")
 #' is strongly recommended. If the serial distribution is unknown (i.e., \code{mu} is \code{NA}), the
 #' likelihood function can be flat near the maximum, resulting in numerical instability of the optimizer.
 #' When \code{mu} is \code{NA}, the implementation takes considerably longer to run. Users should be careful
 #' is strongly recommended. If the serial distribution is unknown (i.e., \code{mu} is \code{NA}), the
 #' likelihood function can be flat near the maximum, resulting in numerical instability of the optimizer.
 #' When \code{mu} is \code{NA}, the implementation takes considerably longer to run. Users should be careful

-#' about units of time (e.g. are counts observed daily or weekly?) when implementing.  
+#' about units of time (e.g., are counts observed daily or weekly?) when implementing.  

 #'
 #' The model developed in White and Pagano (2008) is discrete, and hence the serial distribution is finite 
 #' discrete. In our implementation, the input value \code{mu} is that of a continuous distribution. The
 #'
 #' The model developed in White and Pagano (2008) is discrete, and hence the serial distribution is finite 
 #' discrete. In our implementation, the input value \code{mu} is that of a continuous distribution. The


@@ -28,27 +28,26 @@ source("WP_unknown.R")
 #' user notices that the input \code{mu} and output mean of \code{SD} are different, this is to be expected,
 #' and is caused by the discretization.
 #'
 #' user notices that the input \code{mu} and output mean of \code{SD} are different, this is to be expected,
 #' and is caused by the discretization.
 #'

-#' @param NT Vector of case counts
+#' @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). The default value of \code{mu} is set to \code{NA}.
 #' @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). The default value of \code{mu} is set to \code{NA}.

-#' @param search List of default values for the grid search algorithm; the list includes three elements: the
-#'               first is \code{B} which is the length of the grid in one dimension, the second is
-#'               \code{scale.max} which is the largest possible value of the scale parameter, and the third is
-#'               \code{shape.max} which is the largest possible value of the shape parameter; defaults to
+#' @param search List of default values for the grid search algorithm. The list includes three elements: the
+#'               first is \code{B}, which is the length of the grid in one dimension; the second is
+#'               \code{scale.max}, which is the largest possible value of the scale parameter; and the third
+#'               is \code{shape.max}, which is the largest possible value of the shape parameter. Defaults to

 #'               \code{B=100, scale.max=10, shape.max=10}. For both shape and scale, the smallest possible
 #'               \code{B=100, scale.max=10, shape.max=10}. For both shape and scale, the smallest possible

-#'               value is 1/\code{B}.  
+#'               value is 1/\code{B}.

 #' @param tol Cutoff value for cumulative distribution function of the pre-discretization gamma serial
 #' @param tol Cutoff value for cumulative distribution function of the pre-discretization gamma serial

-#'            distribution, defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the
+#'            distribution. Defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the

 #'            original gamma distribution has cumulative probability of no more than 0.999 at this maximum).
 #'
 #'            original gamma distribution has cumulative probability of no more than 0.999 at this maximum).
 #'

-#' @return WP returns a list containing the following components:  \code{Rhat} is the estimate of R0, \code{SD}
-#'            is either the discretized serial distribution (if \code{mu} is not \code{NA}) or the estimated
-#'            discretized serial distribution (if \code{mu} is \code{NA}), and \code{inputs} is a list of the
-#'            original input variables \code{NT, mu, method, search, tol}. The list also returns the variable
-#'            \code{check}, which is equal to the number of non-unique maximum likelihood estimators. The serial
-#'            distribution \code{SD} is returned as a list made up of \code{supp} the support of the distribution
-#'            and \code{pmf} the probability mass function.  
+#' @return \code{WP} returns a list containing the following components:  \code{Rhat} is the estimate of R0,
+#'         and \code{SD} is either the discretized serial distribution (if \code{mu} is not \code{NA}), or the
+#'         estimated discretized serial distribution (if \code{mu} is \code{NA}). The list also returns the
+#'         variable \code{check}, which is equal to the number of non-unique maximum likelihood estimators.
+#'         The serial distribution \code{SD} is returned as a list made up of \code{supp} (the support of
+#'         the distribution) and \code{pmf} (the probability mass function).

 #'
 #' @examples
 #' ## ===================================================== ##
 #'
 #' @examples
 #' ## ===================================================== ##

diff --git a/R/WP_known.R b/R/WP_known.R
index 563b7ae3ab9e3ebb6267ce1e19208336d35cc57a..2c54bce3d48cb2ececf6fb2cbec45de8dd3a5e96 100644 (file)
--- a/R/WP_known.R
+++ b/R/WP_known.R
@@ -3,9 +3,10 @@
 #' 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
-#' @return The function returns \code{Rhat}, the maximum likelihood estimator of R0.
+#' @param NT Vector of case counts.
+#' @param p Discretized version of the serial distribution.
+#'
+#' @return The function returns the maximum likelihood estimator of R0.

 #'
 #' @export
 WP_known <- function(NT, p) {
 #'
 #' @export
 WP_known <- function(NT, p) {

diff --git a/R/WP_unknown.R b/R/WP_unknown.R
index 569a880c612b9221f9b1632b82c18be989e2ddd9..542f0f79a5de3d0a5f2e756efbb5a298e2c83fb5 100644 (file)
--- a/R/WP_unknown.R
+++ b/R/WP_unknown.R
@@ -8,11 +8,11 @@ source("WP_known.R")
 #' The function then implements a simple grid search algorithm to obtain the maximum likelihood estimator
 #' of R0 as well as the gamma parameters.
 #'
 #' 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 search parameter)
-#' @param scale.max maximum scale value (grid search parameter)
-#' @param tol cutoff value for cumulative distribution function of the serial distribution, defaults to 0.999
+#' @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).

 #'
 #' @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
 #'
 #' @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


@@ -20,8 +20,8 @@ source("WP_known.R")
 #'         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
 #'         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. 
+#'         \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.

 #'
 #' @export
 WP_unknown <- function(NT, B=100, shape.max=10, scale.max=10, tol=0.999) {
 #'
 #' @export
 WP_unknown <- function(NT, B=100, shape.max=10, scale.max=10, tol=0.999) {

diff --git a/R/computeLL.R b/R/computeLL.R
index 6acfc786e34401e6f4f43543000f6d76b07a1ec6..904777774d89095b663085e318fe108d5bc5314c 100644 (file)
--- a/R/computeLL.R
+++ b/R/computeLL.R
@@ -2,10 +2,11 @@
 #'
 #' 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
-#' @return The function returns the variable \code{LL} which is the log-likelihood at the input variables and parameters.
+#' @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.

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

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
 #'
 #' ## ============================================================= ##