get_stream_summary provides a summary of monitoring coverage and bycatch by stream

get_stream_summary provides a summary of monitoring coverage and bycatch by stream

get_stream_summary(fitted_model, alpha = 0.05)

Arguments

fitted_model

Data and fitted model returned from fit_bycatch() with multi-stream monitoring

alpha

The alpha level for the credible interval, defaults to 0.05

Value

data frame with summary statistics for each monitoring stream

Examples

# \donttest{
d <- data.frame(
  Year = 2002:2014,
  Takes_obs = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0),
  Sets_obs = c(200, 180, 150, 350, 250, 270, 180, 150, 400, 350, 280, 180, 250),
  Takes_em = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0),
  Sets_em = c(150, 120, 140, 250, 180, 190, 120, 100, 300, 270, 200, 130, 190),
  Sets_total = c(1000, 950, 900, 1800, 1200, 1300, 900, 750, 2000, 1800, 1400, 950, 1300)
)

fit <- fit_bycatch(Takes_obs ~ 1,
  data = d, time = "Year",
  effort = "Sets_obs",
  takes_em = "Takes_em",
  effort_em = "Sets_em",
  effort_total = "Sets_total",
  family = "poisson"
)
#> Observer stream: 13 observations
#> Total takes: 3
#> Total effort: 3190
#> EM stream: 13 observations
#> Total takes: 1
#> Total effort: 2340
#> Total fishery effort: 16250
#> Observed effort: 5530
#> Unobserved effort: 10720
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.05 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)
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#> Chain 1: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.004 seconds (Warm-up)
#> Chain 1:                0.004 seconds (Sampling)
#> Chain 1:                0.008 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
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#> Chain 2: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.004 seconds (Warm-up)
#> Chain 2:                0.004 seconds (Sampling)
#> Chain 2:                0.008 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
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#> Chain 3: Iteration: 100 / 1000 [ 10%]  (Warmup)
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#> Chain 3: Iteration: 1000 / 1000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.004 seconds (Warm-up)
#> Chain 3:                0.004 seconds (Sampling)
#> Chain 3:                0.008 seconds (Total)
#> Chain 3: 

get_stream_summary(fit)
#>            stream effort observed_takes estimated_mean estimated_low
#> 1        Observer   3190              3          3.000            NA
#> 2              EM   2340              1          1.000            NA
#> 3 Pooled Observed   5530              4          4.000            NA
#> 4      Unobserved  10720             NA          8.686             1
#> 5   Total Fishery  16250             NA         12.686             5
#>   estimated_high coverage_pct
#> 1             NA         19.6
#> 2             NA         14.4
#> 3             NA         34.0
#> 4             22         66.0
#> 5             26        100.0
# }