Added documentation for data sets.

man/DPModel_impl.Rd

@@ -3,36 +3,56 @@
 \docType{data}
 \name{DPModel_impl}
 \alias{DPModel_impl}
-\title{Builds the source attribution model. Is not intended to be used by a regular user.  Developers only here!}
-\format{An object of class \code{R6ClassGenerator} of length 24.}
+\title{Builds the source attribution model. Is not intended to be used by a regular user.
+Developers only here!}
+\format{\code{\link{R6Class}} object.}
 \usage{
 DPModel_impl
 }
-\arguments{
-\item{y}{3D array of [type, time, location] of the number of human cases}
+\value{
+Object of \code{\link{R6Class}}.
+}
+\description{
+Builds the source attribution model. Is not intended to be used by a regular user.
+Developers only here!
+}
+\section{Fields}{
 
-\item{R}{3D array of normalised relative prevalences for each timepoint [type, source, time]}
+\describe{
+\item{\code{y}}{3D array of [type, time, location] of the number of human cases}
 
-\item{Time}{a character vector of timepoint ids matching time dimension in y and R}
+\item{\code{X}}{3D array of the number of positive samples for each type, source and time
+[type, source, time]}
 
-\item{Location}{a character vector of location ids matching location dimension in y}
+\item{\code{R}}{3D array of normalised relative prevalences for each timepoint
+[type, source, time]}
 
-\item{Prev}{a 2D array (matrix) of [source, time].}
+\item{\code{Time}}{a character vector of timepoint ids matching time dimension in y and R}
 
-\item{a_q}{concentration parameter for the DP}
+\item{\code{Location}}{a character vector of location ids matching location dimension in y}
 
-\item{a_theta}{shape parameter for the Gamma base distribution for the DP}
+\item{\code{Sources}}{a character vector of source ids matching the source dimension in X}
 
-\item{b_theta}{rate parameter for the Gamma base distribution for the DP}
+\item{\code{Type}}{a character vector of type ids matching the type dimension in X}
 
-\item{s}{vector giving group allocation for each type for the DP}
+\item{\code{prev}}{a 2D array (matrix) of [source, time].}
 
-\item{theta}{vector giving values for each group in the DP}
+\item{\code{a_q}}{concentration parameter for the DP}
 
-\item{a_r}{3D array of [type, src, time] for the hyperprior on the relative prevalences R}
-}
-\description{
-Builds the source attribution model. Is not intended to be used by a regular user.  Developers only here!
-}
+\item{\code{a_theta}}{shape parameter for the Gamma base distribution for the DP}
+
+\item{\code{b_theta}}{rate parameter for the Gamma base distribution for the DP}
+
+\item{\code{a_r}}{3D array of [type, src, time] for the hyperprior on the relative prevalences R}
+
+\item{\code{a_alpha}}{3D array of [source, time, location] for the prior on the alpha parameters}
+
+\item{\code{s}}{vector giving initial group allocation for each type for the DP}
+
+\item{\code{theta}}{vector giving initial values for each group in the DP}
+
+\item{\code{alpha}}{3D array of [source, time, location] giving initial values for the alpha
+parameters}
+}}
 \keyword{datasets}
 

man/HaldDP.Rd

@@ -216,8 +216,8 @@ divided by the number of negative samples) \eqn{j}
 \deqn{r_{ijt}}  is the unknown relative occurrence of type \eqn{i} on source \eqn{j}.
 
 \emph{Priors}
-\deqn{r_{.jt}\sim Dirichlet(a_r_{1jt},..., a_r_{njt})}
-\deqn{a_{tl}\sim Dirichlet(a_alpha_{1tl},..., a_alpha_{mtl})}
+\deqn{r_{.jt}\sim Dirichlet(a\_r_{1jt},..., a\_r_{njt})}
+\deqn{a_{tl}\sim Dirichlet(a\_alpha_{1tl},..., a\_alpha_{mtl})}
 \deqn{q\sim DP(a_q, Gamma(a_{theta},b_{theta}))}
 }
 }
@@ -235,19 +235,19 @@ res <- HaldDP$new(data = campy, k = prevs, priors = priors, a_q = 1)
 res$fit_params(n_iter = 100, burn_in = 10, thin = 1)
 res$update()
 
-res$print_data()
-res$print_inits()
-res$print_priors()
-res$print_acceptance()
-res$print_fit_params()
+dat <- res$print_data()
+init <- res$print_inits()
+prior <- res$print_priors()
+acceptance <- res$print_acceptance()
+fit_params <- res$print_fit_params()
 
 res$plot_heatmap(iters = 10:100, hclust_method = "complete")
 
-res$summary(params = c("alpha", "q", "lambda_i"),
+summarys <- res$summary(params = c("alpha", "q", "lambda_i"),
             times = "1", sources = c("ChickenA", "Bovine"),
             iters = 10:100, flatten = TRUE, CI_type = "chen-shao")
 
-res$extract(params = c("alpha", "r", "q", "lambda_j"),
+posteriors <- res$extract(params = c("alpha", "r", "q", "lambda_j"),
             sources = c("ChickenB", "Ovine"),
             types = c("474", "52"),
             iters = 50:100, drop = FALSE, flatten = FALSE)
@@ -256,9 +256,11 @@ res$extract(params = c("alpha", "r", "q", "lambda_j"),
 Chris Jewell and Poppy Miller \email{p.miller at lancaster.ac.uk}
 }
 \references{
-Chen, M.-H. and Shao, Q.-M. (1998). Monte Carlo estimation of Bayesian credible and HPD intervals, \emph{Journal of Computational and Graphical Statistics}, 7.
+Chen, M.-H. and Shao, Q.-M. (1998). Monte Carlo estimation of Bayesian
+credible and HPD intervals, \emph{Journal of Computational and Graphical Statistics}, 7.
 
-Liu Y, Gelman A, Zheng T (2015). "Simulation-efficient shortest probability intervals." Statistics and Computing.
+Liu Y, Gelman A, Zheng T (2015). "Simulation-efficient shortest probability
+intervals." Statistics and Computing.
 }
 \keyword{datasets}