plot_fitted makes plots bycatch estimates (lambda of Poisson), accounting for effort but not accounting for observer coverage
Source:R/plot_fitted.R
plot_fitted.Rdplot_fitted makes plots bycatch estimates (lambda of Poisson), accounting for effort but not accounting for observer coverage
plot_fitted(
fitted_model,
xlab = "Time",
ylab = "Events",
include_points = FALSE,
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).
- xlab
X-axis label for plot
- ylab
Y-axis label for plot
- include_points
whether or not to include raw bycatch events on plots, defaults to FALSE
- 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).
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plot_fitted(fit,
xlab = "Year", ylab = "Fleet-level bycatch",
include_points = TRUE
)
# fit a negative binomial model, with more chains and control arguments
fit_nb <- fit_bycatch(Takes ~ 1,
data = d, time = "Year",
effort = "Sets", family = "nbinom2",
time_varying = FALSE, iter = 2000, chains = 4,
control = list(adapt_delta = 0.99, max_treedepth = 20)
)
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
# fit a time varying model
fit <- fit_bycatch(Takes ~ 1,
data = d, time = "Year",
effort = "Sets", family = "poisson", time_varying = TRUE
)
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Warning: There were 4 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
# include data for expansion to unobserved sets
fit_nb <- fit_bycatch(Takes ~ 1,
data = d, time = "Year",
effort = "Sets", family = "nbinom2",
expansion_rate = "expansionRate",
time_varying = FALSE, iter = 2000, chains = 4,
control = list(adapt_delta = 0.99, max_treedepth = 20)
)
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 1: Adjust your expectations accordingly!
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# }