fit_bycatch is the primary function for fitting bycatch models to time series of takes and effort
Source:R/fitting.R
fit_bycatch.Rdfit_bycatch is the primary function for fitting bycatch models to time series of takes and effort
fit_bycatch(
formula,
data,
time = "year",
effort = "effort",
expansion_rate = NULL,
family = c("poisson", "nbinom2", "poisson-hurdle", "nbinom2-hurdle", "lognormal",
"gamma", "lognormal-hurdle", "gamma-hurdle", "normal", "normal-hurdle"),
time_varying = FALSE,
iter = 1000,
chains = 3,
control = list(adapt_delta = 0.9, max_treedepth = 20),
...
)Arguments
- formula
The model formula.
- data
A data frame.
- time
Named column of the 'data' data frame with the label for the time (e.g. year) variable
- effort
Named column of the 'effort' variable in the data frame with the label for the fishing effort to be used in estimation of mean bycatch rate. This represents total observed effort
- expansion_rate
The expansion rate to be used in generating distributions for unobserved sets. If NULL, defaults to 100% coverage (= 100)
- family
Family for response distribution can be discrete ("poisson", "nbinom2", "poisson-hurdle","nbinom2-hurdle"), or continuous ("normal", "gamma","lognormal", "normal-hurdle", "gamma-hurdle", "lognormal-hurdle"). The default distribution is "poisson". The hurdle variants estimate the probability of zeros (theta) separately from the other models and use truncated distribution to model positive counts. All use a log link function.
- time_varying
boolean TRUE/FALSE, whether to include time varying component (this is a random walk, analogous to making this a Dynamic linear model)
- iter
the number of mcmc iterations, defaults to 1000
- chains
the number of mcmc chains, defaults to 3
- control
List to pass to
rstan::sampling(). For example, increaseadapt_deltaif there are warnings about divergent transitions:control = list(adapt_delta = 0.99). By default, bycatch setsadapt_delta = 0.9.- ...
Any other arguments to pass to
rstan::sampling().
Value
list of the data used to fit the model, the matrix of covariates, the expanded bycatch generated via the fit and simulations, and the fitted stan model
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 4.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 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: 0.013 seconds (Warm-up)
#> Chain 1: 0.013 seconds (Sampling)
#> Chain 1: 0.026 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: 0.012 seconds (Warm-up)
#> Chain 2: 0.011 seconds (Sampling)
#> Chain 2: 0.023 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: 0.011 seconds (Sampling)
#> Chain 3: 0.025 seconds (Total)
#> Chain 3:
loo::loo(fit$fitted_model)$estimates
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> Estimate SE
#> elpd_loo -11.143287 5.860407
#> p_loo 2.503908 2.091048
#> looic 22.286574 11.720815
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)
#> 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: 0.012 seconds (Warm-up)
#> Chain 1: 0.014 seconds (Sampling)
#> Chain 1: 0.026 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: 0.013 seconds (Warm-up)
#> Chain 2: 0.014 seconds (Sampling)
#> Chain 2: 0.027 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)
#> 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: 0.013 seconds (Warm-up)
#> Chain 3: 0.014 seconds (Sampling)
#> Chain 3: 0.027 seconds (Total)
#> Chain 3:
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 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.13 seconds (Warm-up)
#> Chain 1: 0.116 seconds (Sampling)
#> Chain 1: 0.246 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.09 seconds (Warm-up)
#> Chain 2: 0.083 seconds (Sampling)
#> Chain 2: 0.173 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.122 seconds (Warm-up)
#> Chain 3: 0.087 seconds (Sampling)
#> Chain 3: 0.209 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.103 seconds (Warm-up)
#> Chain 4: 0.109 seconds (Sampling)
#> Chain 4: 0.212 seconds (Total)
#> Chain 4:
# 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 3.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.31 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: 0.155 seconds (Warm-up)
#> Chain 1: 0.13 seconds (Sampling)
#> Chain 1: 0.285 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.12 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: 0.186 seconds (Warm-up)
#> Chain 2: 0.114 seconds (Sampling)
#> Chain 2: 0.3 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 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: 0.199 seconds (Warm-up)
#> Chain 3: 0.185 seconds (Sampling)
#> Chain 3: 0.384 seconds (Total)
#> Chain 3:
#> Warning: There were 2 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: Examine the pairs() plot to diagnose sampling problems
#> 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 1.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.12 seconds (Warm-up)
#> Chain 1: 0.14 seconds (Sampling)
#> Chain 1: 0.26 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.124 seconds (Warm-up)
#> Chain 2: 0.127 seconds (Sampling)
#> Chain 2: 0.251 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.103 seconds (Warm-up)
#> Chain 3: 0.113 seconds (Sampling)
#> Chain 3: 0.216 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.124 seconds (Warm-up)
#> Chain 4: 0.14 seconds (Sampling)
#> Chain 4: 0.264 seconds (Total)
#> Chain 4:
# fit a model with a lognormal distribution
d$Takes <- rnorm(nrow(d), 5, 0.1)
fit_ln <- fit_bycatch(Takes ~ 1,
data = d, time = "Year",
effort = "Sets", family = "lognormal",
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 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.07 seconds (Warm-up)
#> Chain 1: 0.057 seconds (Sampling)
#> Chain 1: 0.127 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.073 seconds (Warm-up)
#> Chain 2: 0.073 seconds (Sampling)
#> Chain 2: 0.146 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 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.074 seconds (Warm-up)
#> Chain 3: 0.054 seconds (Sampling)
#> Chain 3: 0.128 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.075 seconds (Warm-up)
#> Chain 4: 0.067 seconds (Sampling)
#> Chain 4: 0.142 seconds (Total)
#> Chain 4:
# add zeros and fit a delta-gamma distribution
d$Takes <- rnorm(nrow(d), 5, 0.1)
d$Takes[c(1, 5, 10)] <- 0
fit_ln <- fit_bycatch(Takes ~ 1,
data = d, time = "Year",
effort = "Sets", family = "gamma-hurdle",
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 2.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.132 seconds (Warm-up)
#> Chain 1: 0.113 seconds (Sampling)
#> Chain 1: 0.245 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.168 seconds (Warm-up)
#> Chain 2: 0.152 seconds (Sampling)
#> Chain 2: 0.32 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.153 seconds (Warm-up)
#> Chain 3: 0.129 seconds (Sampling)
#> Chain 3: 0.282 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.172 seconds (Warm-up)
#> Chain 4: 0.133 seconds (Sampling)
#> Chain 4: 0.305 seconds (Total)
#> Chain 4:
# }