plot_indices {bbsBayes}R Documentation

Generate plots of index trajectories by stratum


Generates the indices plot for each stratum modelled.


  indices_list = NULL,
  ci_width = 0.95,
  min_year = NULL,
  max_year = NULL,
  species = "",
  title_size = 20,
  axis_title_size = 18,
  axis_text_size = 16,
  line_width = 1,
  add_observed_means = FALSE,
  add_number_routes = FALSE



List of indices of annual abundance and other results produced by generate_strata_indices


quantile to define the width of the plotted credible interval. Defaults to 0.95, lower = 0.025 and upper = 0.975


Minimum year to plot


Maximum year to plot


Species name to be added onto the plot


Specify font size of plot title. Defaults to 20


Specify font size of axis titles. Defaults to 18


Specify font size of axis text. Defaults to 16


Specify the size of the trajectory line. Defaults to 1


Should the plot include points indicated the observed mean counts. Defaults to FALSE. Note: scale of observed means and annual indices may not match due to imbalanced sampling among routes


Should the plot be superimposed over a dotplot showing the number of BBS routes included in each year. This is useful as a visual check on the relative data-density through time because in most cases the number of observations increases over time


List of ggplot objects, each entry being a plot of a stratum indices


# Toy example with Pacific Wren sample data
# First, stratify the sample data

strat_data <- stratify(by = "bbs_cws", sample_data = TRUE)

# Prepare the stratified data for use in a JAGS model.
jags_data <- prepare_jags_data(strat_data = strat_data,
                               species_to_run = "Pacific Wren",
                               model = "firstdiff",
                               min_year = 2009,
                               max_year = 2018)

# Now run a JAGS model.
jags_mod <- run_model(jags_data = jags_data,
                      n_adapt = 0,
                      n_burnin = 0,
                      n_iter = 10,
                      n_thin = 1)

# Generate only national, continental, and stratum indices
indices <- generate_indices(jags_mod = jags_mod,
                            jags_data = jags_data,
                            regions = c("national",

# Now, plot_indices() will generate a list of plots for all regions
plot_list <- plot_indices(indices_list = indices,
                          species = "Pacific Wren")

#Suppose we wanted to access the continental plot. We could do so with
cont_plot <- plot_list$continental

# You can specify to only plot a subset of years using min_year and max_year
# Plots indices from 2015 onward
plot_list_2015_on <- plot_indices(indices_list = indices,
                                  min_year = 2015,
                                  species = "Pacific Wren")

#Plot up indices up to the year 2017
plot_list_max_2017 <- plot_indices(indices_list = indices,
                                   max_year = 2017,
                                   species = "Pacific Wren")

#Plot indices between 2011 and 2016
plot_list_2011_2015 <- plot_indices(indices_list = indices,
                                    min_year = 2011,
                                    max_year = 2016,
                                    species = "Pacific Wren")

[Package bbsBayes version 2.5.2 Index]