revert back to itemize tag; new example for htmlSummary

R/WSS.r

@@ -32,7 +32,7 @@
 #'         
 #'         The data.frame has a number of attributes:
 #' 
-#'          \describe{
+#'          \itemize{
 #'          \item{\code{intercept_year}}{ - The year used for the intercept (i.e. the
 #'           year whose value is set to 0). Setting the intercept to the median year helps
 #'           to increase model stability}

R/htmlSummary.R

@@ -10,8 +10,50 @@
 #' @param ... Additional arguments passed to rmarkdown::render
 #' 
 #' @return Path to html report
-#' @export
 #' @import rmarkdown
+#' 
+#' @examples
+#' 
+#' \dontrun{
+#' # Create data
+#' set.seed(125)
+#' n <- 15000 #size of dataset
+#' nyr <- 20 # number of years in data
+#' nSamples <- 100 # set number of dates
+#' nSites <- 50 # set number of sites
+#' 
+#' # Create somes dates
+#' first <- as.Date(strptime("1980/01/01", "%Y/%m/%d")) 
+#' last <- as.Date(strptime(paste(1980+(nyr-1),"/12/31", sep=''), "%Y/%m/%d")) 
+#' dt <- last-first 
+#' rDates <- first + (runif(nSamples)*dt)
+#' 
+#' # taxa are set as random letters
+#' taxa <- sample(letters, size = n, TRUE)
+#' 
+#' # three sites are visited randomly
+#' site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)
+#' 
+#' # the date of visit is selected at random from those created earlier
+#' survey <- sample(rDates, size = n, TRUE)
+#' 
+#' # run the model with these data for one species
+#' # using defaults
+#' results <- occDetModel(taxa = taxa,
+#'                        site = site,
+#'                        survey = survey,
+#'                        species_list = 'a',
+#'                        write_results = TRUE,
+#'                        n_iterations = 1000,
+#'                        burnin = 10,
+#'                        thinning = 2)
+#' 
+#' generate summary
+#' htmlSummary(results$a)
+#' 
+#' }
+#' 
+#' @export
 
 htmlSummary <- function(occDet,
                         open = TRUE,

R/occDetFunc.r

@@ -79,7 +79,7 @@
 #' a combination that is not supported. There is usually a good reason why that
 #' combination is not a good idea. Here are the model elements available:
 #'
-#' \describe{
+#' \itemize{
 #'  \item{\code{"sparta"}}{ - This uses the same model as in Isaac et al (2014)}
 #'  \item{\code{"indran"}}{ - Here the prior for the year effect of the state model is modelled as a random effect.  This allows the model to adapt to interannual variability.}
 #'  \item{\code{"ranwalk"}}{ - Here the prior for the year effect of the state model is modelled as a random walk.  Each estimate for the year effect is dependent on that of the previous year.}
@@ -96,7 +96,7 @@
 #'
 #' @return A list including the model, JAGS model output, the path of the model file used and information on the number of iterations, first year, last year, etc.
 #' Key aspects of the model output include:
-#' \describe{
+#' \itemize{
 #'  \item{\code{"out$model"}}{ - The model used as provided to JAGS. Also contained is a list of fully observed variables. These are those listed in the BUGS data.}
 #'  \item{\code{"out$BUGSoutput$n.chains"}}{ - The number of Markov chains ran in the MCMC simulations.}
 #'  \item{\code{"out$BUGSoutput$n.iter"}}{ - The total number of iterations per chain.}