plot_indices {bbsBayes}  R Documentation 
Generates the indices plot for each stratum modelled.
plot_indices( 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, add_observed_means = FALSE, add_number_routes = FALSE )
indices_list 
List of indices of annual abundance and other results produced by

ci_width 
quantile to define the width of the plotted credible interval. Defaults to 0.95, lower = 0.025 and upper = 0.975 
min_year 
Minimum year to plot 
max_year 
Maximum year to plot 
species 
Species name to be added onto the plot 
title_size 
Specify font size of plot title. Defaults to 20 
axis_title_size 
Specify font size of axis titles. Defaults to 18 
axis_text_size 
Specify font size of axis text. Defaults to 16 
add_observed_means 
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 
add_number_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 datadensity 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", "continental", "stratum")) # 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")