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,
line_width = 1,
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 
line_width 
Specify the size of the trajectory line. Defaults to 1 
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")