plot_expanded is makes plots of the expanded bycatch estimates, accounting for observer coverage and effort

plot_expanded is makes plots of the expanded bycatch estimates, accounting for observer coverage and effort

plot_expanded(
  fitted_model,
  xlab = "Time",
  ylab = "Events",
  show_total = TRUE,
  include_points = FALSE
)

Arguments

fitted_model

Data and fitted model returned from estimation

xlab

X-axis label for plot

ylab

Y-axis label for plot

show_total

Whether to show the total predicted bycatch (by default, this is TRUE) or just the expanded unobserved events (=FALSE)

include_points

whether or not to include raw bycatch events on plots, defaults to FALSE

Value

plot called from ggplot

Examples

# \donttest{
d <- data.frame(
  "Year" = 2002:2014,
  "Takes" = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0),
  "expansionRate" = c(24, 22, 14, 32, 28, 25, 30, 7, 26, 21, 22, 23, 27),
  "Sets" = c(391, 340, 330, 660, 470, 500, 330, 287, 756, 673, 532, 351, 486)
)
fit <- fit_bycatch(Takes ~ 1,
  data = d, time = "Year",
  effort = "Sets",
  family = "poisson",
  expansion_rate = "expansionRate",
  time_varying = FALSE
)
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 9e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 1: Iteration: 100 / 1000 [ 10%]  (Warmup)
#> Chain 1: Iteration: 200 / 1000 [ 20%]  (Warmup)
#> Chain 1: Iteration: 300 / 1000 [ 30%]  (Warmup)
#> Chain 1: Iteration: 400 / 1000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 500 / 1000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 501 / 1000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 600 / 1000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 700 / 1000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 800 / 1000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 900 / 1000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.015 seconds (Warm-up)
#> Chain 1:                0.013 seconds (Sampling)
#> Chain 1:                0.028 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 7e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 2: Iteration: 100 / 1000 [ 10%]  (Warmup)
#> Chain 2: Iteration: 200 / 1000 [ 20%]  (Warmup)
#> Chain 2: Iteration: 300 / 1000 [ 30%]  (Warmup)
#> Chain 2: Iteration: 400 / 1000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 500 / 1000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 600 / 1000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 700 / 1000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 800 / 1000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 900 / 1000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.014 seconds (Warm-up)
#> Chain 2:                0.012 seconds (Sampling)
#> Chain 2:                0.026 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:   1 / 1000 [  0%]  (Warmup)
#> Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
#> Chain 3: Iteration: 200 / 1000 [ 20%]  (Warmup)
#> Chain 3: Iteration: 300 / 1000 [ 30%]  (Warmup)
#> Chain 3: Iteration: 400 / 1000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 500 / 1000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 501 / 1000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 600 / 1000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 700 / 1000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 800 / 1000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 900 / 1000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.014 seconds (Warm-up)
#> Chain 3:                0.012 seconds (Sampling)
#> Chain 3:                0.026 seconds (Total)
#> Chain 3: 
plot_expanded(
  fitted_model = fit,
  xlab = "Year",
  ylab = "Fleet-level bycatch",
  include_points = TRUE
)

# }