R/get_expanded.R
get_expanded.Rdget_expanded is a helper function to return a matrix of posterior predictive values for unobserved bycatch
get_expanded(fitted_model)Data and fitted model returned from estimation
matrix (MCMC draws x time steps) of posterior predictive values for unobserved bycatch
# \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",
expansion_rate = "expansionRate",
time_varying = FALSE
)
#>
#> SAMPLING FOR MODEL 'bycatch' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.016 seconds (Warm-up)
#> Chain 1: 0.014 seconds (Sampling)
#> Chain 1: 0.03 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!
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#> Chain 2: Elapsed Time: 0.018 seconds (Warm-up)
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#> Chain 2: 0.031 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:
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.023 seconds (Warm-up)
#> Chain 3: 0.015 seconds (Sampling)
#> Chain 3: 0.038 seconds (Total)
#> Chain 3:
expanded <- get_expanded(fit)
# }