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Generate plots of index trajectories by stratum

Source: R/plot-indices.R
plot_indices.Rd

Generates the indices plot for each stratum modelled.

Usage

plot_indices(
  indices = NULL,
  ci_width = 0.95,
  min_year = NULL,
  max_year = NULL,
  title = TRUE,
  title_size = 20,
  axis_title_size = 18,
  axis_text_size = 16,
  line_width = 1,
  spaghetti = FALSE,
  n_spaghetti = 100,
  alpha_spaghetti = 0.2,
  add_observed_means = FALSE,
  add_number_routes = FALSE
)

Arguments

indices

List. Indices generated by generate_indices().

ci_width

Numeric. Quantile defining the width of the plotted credible interval. Defaults to 0.95 (lower = 0.025 and upper = 0.975). Note these quantiles need to have been precalculated in generate_indices().

min_year

Numeric. Minimum year to plot.

max_year

Numeric. Maximum year to plot.

title

Logical. Whether to include a title on the plot.

title_size

Numeric. Font size of plot title. Defaults to 20

axis_title_size

Numeric. Font size of axis titles. Defaults to 18

axis_text_size

Numeric. Font size of axis text. Defaults to 16

line_width

Numeric. Size of the trajectory line. Defaults to 1

spaghetti

Logical. False by default. Plotting option to visualise the uncertainty in the estimated population trajectories. Instead of plotting the trajectory as a single line with an uncertainty bound, if TRUE, then the plot shows a sample of the posterior distribution of the estimated population trajectories. E.g., 100 semi-transparent lines, each representing one posterior draw of the population trajectory.

n_spaghetti

Integer. 100 by default. Number of posterior draws of the population trajectory to include in the plot. Ignored if spaghetti = FALSE.

alpha_spaghetti

Numeric between 0 and 1, 0.2 by default. Alpha value - transparency of each individual population trajectory line in the spaghetti plot. Ignored if spaghetti = FALSE.

add_observed_means

Logical. Whether to include points indicating the observed mean counts. Default FALSE. Note: scale of observed means and annual indices may not match due to imbalanced sampling among routes. Also, pattern of change in observed means through time should reflect estimated population trajectories for strata-level summaries. However, the temporal patterns may be very different between means and annual indices for composite regions (e.g., continental, state, province, or BCR) because the indices for composite regions account for the variation in area weights and variation in relative abundances among strata, and the observed mean counts do not. For strata-level assessments, these observed means can be a useful model assessment tool. For composite regions, their interpretation is more complicated.

add_number_routes

Logical. Whether to superimpose 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.

Value

List of ggplot2 plots, each item being a plot of a stratum's indices.

See also

Other indices and trends functions: generate_indices(), generate_trends(), plot_geofacet(), plot_map()

Examples

# Using the example model for Pacific Wrens...

# Generate country, continent, and stratum indices
i <- generate_indices(model_output = pacific_wren_model,
                      regions = c("country", "continent", "stratum"))
#> Processing region country
#> Processing region continent
#> Processing region stratum

# Now, plot_indices() will generate a list of plots for all regions
plots <- plot_indices(i)

# To view any plot, use [[i]]
plots[[1]]


names(plots)
#>  [1] "Canada"                   "United_States_of_America"
#>  [3] "continent"                "CA_AB_10"                
#>  [5] "CA_BC_10"                 "CA_BC_4"                 
#>  [7] "CA_BC_5"                  "CA_BC_9"                 
#>  [9] "US_AK_2"                  "US_AK_4"                 
#> [11] "US_AK_5"                  "US_CA_15"                
#> [13] "US_CA_32"                 "US_CA_5"                 
#> [15] "US_ID_10"                 "US_MT_10"                
#> [17] "US_OR_10"                 "US_OR_5"                 
#> [19] "US_OR_9"                  "US_WA_10"                
#> [21] "US_WA_5"                  "US_WA_9"                 

# Suppose we wanted to access the continental plot. We could do so with
plots[["continent"]]


# You can specify to only plot a subset of years using min_year and max_year

# Plots indices from 2015 onward
p_2015_min <- plot_indices(i, min_year = 2015)
p_2015_min[["continent"]]


#Plot up indices up to the year 2017
p_2017_max <- plot_indices(i, max_year = 2017)
p_2017_max[["continent"]]


#Plot indices between 2011 and 2016
p_2011_2016 <- plot_indices(i, min_year = 2011, max_year = 2016)
p_2011_2016[["continent"]]