more ex speedup

R/bsam-package.R

@@ -37,8 +37,8 @@
 #' \dontrun{
 #' # Fit DCRW model for state filtering and regularization -
 #' # using trivial adapt & samples values for speed
-#' data(ellie)
-#' fit <- fit_ssm(ellie, model = "DCRW", tstep = 1, adapt = 100, samples = 100, 
+#' data(ellie1)
+#' fit <- fit_ssm(ellie1, model = "DCRW", tstep = 1, adapt = 10, samples = 100, 
 #'               thin = 1, span = 0.2)
 #' diag_ssm(fit)
 #' map_ssm(fit)
@@ -54,9 +54,9 @@ NULL
 
 
 
-##' @name ellie
+##' @name ellie1
 ##' @docType data
-##' @title Elephant seal Argos satellite data (2 individuals)
+##' @title Elephant seal Argos satellite data (1 individual)
 ##' @format .RData
 ##' @keywords data 
 ##' @description Example elephant seal Argos tracking data. Data were sourced from 
@@ -65,4 +65,13 @@ NULL
 ##' Strategy and the Super Science Initiative.
 NULL
 
-
+##' @name ellie2
+##' @docType data
+##' @title Elephant seal Argos satellite data (2 individuals)
+##' @format .RData
+##' @keywords data 
+##' @description Example elephant seal Argos tracking data. Data were sourced from 
+##' the Integrated Marine Observing System (IMOS) - IMOS is supported by the 
+##' Australian Government through the National Collaborative Research Infrastructure 
+##' Strategy and the Super Science Initiative.
+NULL

R/diag_ssm.R

@@ -12,18 +12,6 @@
 #' increasing number of iterations. See the \code{coda} package for further details.
 #' @references Brooks SP, Gelman A (1998) General methods for monitoring convergence of 
 #' iterative simulations. Journal of Computational and Graphical Statistics 7:434-455
-#' @examples
-#' \dontrun{
-#' data(ellie)
-#' fit.s <- fit_ssm(ellie, model = "DCRWS", tstep = 1, adapt = 100, samples = 100, 
-#'               thin = 1, span = 0.1)
-#' diag_ssm(fit.s)
-#' 
-#' # increase burnin, posterior sample numbers, and thinning factor
-#' fit.s2 <- fit_ssm(ellie, model = "DCRWS", tstep = 1, adapt = 100, samples = 100, 
-#'               thin = 1, span = 0.1)
-#' diag_ssm(fit.s2)
-#' }             
 #' @importFrom coda autocorr.plot traceplot gelman.plot as.mcmc.list nvar varnames<-
 #' @export 
 #' 

R/fit_ssm.R

@@ -69,18 +69,14 @@
 #' 
 #' # Fit DCRW model for state filtering and regularization - 
 #' # using trivial adapt & samples values for speed
-#' data(ellie)
-#' fit <- fit_ssm(ellie, model = "DCRW", tstep = 2, adapt = 100, samples = 100, 
+#' data(ellie1)
+#' fit <- fit_ssm(ellie1, model = "DCRW", tstep = 4, adapt = 10, samples = 100, 
 #'               thin = 1, span = 0.2)
-#' diag_ssm(fit)
-#' map_ssm(fit)
-#' plot_fit(fit)
-#' result <- get_summary(fit)
 #' 
-#' \dontrun{
+#' \donttest{
 #' # Fit DCRWS model for state filtering, regularization and behavioural state estimation - 
 #' # using trivial adapt & samples values for speed
-#'  fit.s <- fit_ssm(ellie, model = "DCRWS", tstep = 2, adapt = 100, samples = 100, 
+#'  fit.s <- fit_ssm(ellie1, model = "DCRWS", tstep = 2, adapt = 10, samples = 100, 
 #'                 thin = 1, span = 0.2)
 #'  diag_ssm(fit.s)
 #'  map_ssm(fit.s)
@@ -91,12 +87,13 @@
 #' # this may provide better parameter and behavioural state estimation 
 #' # by borrowing strength across multiple track datasets - 
 #' # using trivial adapt & samples values for speed
-#'  hfit.s <- fit_ssm(ellie, model = "hDCRWS", tstep = 2, adapt = 100, samples = 100, 
+#' data(ellie2)
+#' hfit.s <- fit_ssm(ellie2, model = "hDCRWS", tstep = 2, adapt = 10, samples = 100, 
 #'                 thin = 1, span = 0.2)
-#'  diag_ssm(hfit.s)
-#'  map_ssm(hfit.s)
-#'  plot_fit(hfit.s)
-#'  result.hs <- get_summary(hfit.s)
+#' diag_ssm(hfit.s)
+#' map_ssm(hfit.s)
+#' plot_fit(hfit.s)
+#' result.hs <- get_summary(hfit.s)
 #' }
 #' @importFrom tibble as_tibble
 #' @export 

R/get_summary.R

@@ -11,26 +11,6 @@
 #' @return a summary data.frame printed either to the console (default) or written as .csv to a
 #' specified file.
 #' 
-#' @examples
-#' \dontrun{
-#' data(ellie)
-#' fit <- fit_ssm(ellie, model = "DCRW", tstep = 1, adapt = 100, samples = 100, 
-#'                 thin = 1, span = 0.1)
-#'                 
-#' ## print to console                 
-#' get_summary(fit)
-#' 
-#' ## export to .csv file
-#' get_summary(fit, file = "ellie_dcrw.csv")
-#' 
-#' ## plot track of second animal (posterior mean longitude and latitude)
-#' p <- ggplot(data = subset(get_summary(fit), id == unique(id)[2])) + 
-#'        geom_point(aes(lat, lon), col = "firebrick", size = 2)
-#' p
-#' 
-#' ## overlay posterior median longitude and latitude
-#' p + geom_point(aes(lat.5, lon.5), col = "dodgerblue", size = 1)
-#' }
 #' @importFrom utils write.csv
 #' @export 
 

R/plot_fit.R

@@ -8,17 +8,6 @@
 #' @return Observed locations are plotted as filled circles and estimated locations as blue
 #' lines with the 95\% credible interval as a ribbon. Uses \code{ggplot2} plotting functions.
 #' 
-#' @examples
-#' \dontrun{
-#' data(ellie)
-#' fit.s <- fit_ssm(ellie, model = "DCRWS", tstep = 1, adapt = 100, samples = 100, 
-#'               thin = 1, span = 0.1)
-#' plot_fit(fit.s)
-#' 
-#' hfit.s <- fit_ssm(ellie, model = "hDCRWS", tstep = 1, adapt = 100, samples = 100, 
-#'                 thin = 1, span = 0.1)
-#' plot_fit(hfit.s)
-#' }
 #' @importFrom ggplot2 ggplot aes ggtitle geom_point scale_color_gradient2 xlab ylab aes_string 
 #' @importFrom ggplot2 ylim geom_line geom_ribbon
 #' @importFrom gridExtra grid.arrange