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Create geofacet plot of population trajectories by province/state

Source: R/plot-geofacet.R
plot_geofacet.Rd

Generate a faceted plot of population trajectories by province/state. Only possible if indices created by generate_indices() include the prov_state region. All geofacet plots have one facet per state/province, so if there are multiple strata per facet, these can be plotted as separate trajectories within each facet (multiple = TRUE).

Usage

plot_geofacet(
  indices,
  ci_width = 0.95,
  multiple = FALSE,
  trends = NULL,
  slope = FALSE,
  add_observed_means = FALSE,
  col_viridis = 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().

multiple

Logical. Whether to plot multiple strata-level trajectories within each prov/state facet. Default FALSE.

List. (Optional) Output generated by generate_trends(). If included trajectories are coloured based on the same colour scale used in plot_map.

slope

Logical. If trends included, whether colours in the plot should be based on slope trends. Default 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.

col_viridis

Logical. Should the colour-blind-friendly "viridis" palette be used. Default FALSE.

Value

ggplot object

See also

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

Examples

# Using the example model for Pacific Wrens...

# Generate indices
i <- generate_indices(pacific_wren_model,
                      regions = c("stratum", "prov_state"))
#> Processing region stratum
#> Processing region prov_state
# Generate trends
t <- generate_trends(i)

# Now make the geofacet plot.
plot_geofacet(i, trends = t, multiple = TRUE)

plot_geofacet(i, trends = t, multiple = TRUE, col_viridis = TRUE)

plot_geofacet(i, multiple = TRUE)

plot_geofacet(i, trends = t, multiple = FALSE)

plot_geofacet(i, multiple = FALSE)


# With different ci_width, specify desired quantiles in indices
i <- generate_indices(pacific_wren_model,
                      regions = c("stratum", "prov_state"),
                      quantiles = c(0.005, 0.995))
#> Processing region stratum
#> Processing region prov_state

plot_geofacet(i, multiple = FALSE, ci_width = 0.99)