fit_bycatch: fit_bycatch is the primary function for fitting bycatch...

In eric-ward/bycatch: Fit Bayesian Models to Bycatch Data

fit_bycatch is the primary function for fitting bycatch models to time series of takes and effort

Description

fit_bycatch is the primary function for fitting bycatch models to time series of takes and effort

Usage

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, increase adapt_delta if there are warnings about divergent transitions: control = list(adapt_delta = 0.99). By default, bycatch sets adapt_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

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
)
loo::loo(fit$fitted_model)$estimates

fit <- fit_bycatch(Takes ~ 1,
  data = d, time = "Year", effort = "Sets",
  family = "poisson", time_varying = FALSE
)
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)
)

# fit a time varying model
fit <- fit_bycatch(Takes ~ 1,
  data = d, time = "Year",
  effort = "Sets", family = "poisson", time_varying = TRUE
)

# 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)
)

# 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)
)

# 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)
)

eric-ward/bycatch documentation built on July 5, 2023, 4:37 p.m.