get_fitted returns df of observed bycatch estimates (lambda of Poisson), accounting for effort but not accounting for observer coverage

get_fitted returns df of observed bycatch estimates (lambda of Poisson), accounting for effort but not accounting for observer coverage

get_fitted(fitted_model, alpha = 0.05)

Arguments

fitted_model

Data and fitted model returned from fit_bycatch(). If a hurdle model, then only then the plot returns the total bycatch rate (including zero and non-zero components).

alpha

The alpha level for the credible interval, defaults to 0.05

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", 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)
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#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.014 seconds (Warm-up)
#> Chain 1:                0.012 seconds (Sampling)
#> Chain 1:                0.026 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 8e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:   1 / 1000 [  0%]  (Warmup)
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#> Chain 2: Iteration: 501 / 1000 [ 50%]  (Sampling)
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#> Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.012 seconds (Warm-up)
#> Chain 2:                0.013 seconds (Sampling)
#> Chain 2:                0.025 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 7e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.07 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)
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#> 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: 
get_fitted(fit)
#>    time      mean        low      high obs
#> 1  2002 0.2946184 0.09388089 0.5973596   0
#> 2  2003 0.2561899 0.08163556 0.5194431   0
#> 3  2004 0.2486549 0.07923451 0.5041654   0
#> 4  2005 0.4973098 0.15846902 1.0083308   0
#> 5  2006 0.3541449 0.11284915 0.7180537   0
#> 6  2007 0.3767499 0.12005229 0.7638869   0
#> 7  2008 0.2486549 0.07923451 0.5041654   0
#> 8  2009 0.2162544 0.06891001 0.4384711   0
#> 9  2010 0.5696458 0.18151906 1.1549970   1
#> 10 2011 0.5071053 0.16159038 1.0281918   3
#> 11 2012 0.4008619 0.12773563 0.8127757   0
#> 12 2013 0.2644784 0.08427671 0.5362486   0
#> 13 2014 0.3662009 0.11669082 0.7424981   0
# }