Extract a relative biomass/abundance index or a center of gravity
get_index(obj, bias_correct = FALSE, level = 0.95, ...)
get_cog(
obj,
bias_correct = FALSE,
level = 0.95,
format = c("long", "wide"),
...
)Output from predict.sdmTMB() with return_tmb_object = TRUE.
Should bias correction be implemented TMB::sdreport()?
The confidence level.
Passed to TMB::sdreport().
Long or wide.
# \donttest{
if (inla_installed()) {
# Use a small number of knots for this example to make it fast:
pcod_spde <- make_mesh(pcod, c("X", "Y"), n_knots = 60, type = "kmeans")
m <- sdmTMB(
data = pcod,
formula = density ~ 0 + as.factor(year),
time = "year", mesh = pcod_spde, family = tweedie(link = "log")
)
# Note `return_tmb_object = TRUE` and the prediction grid:
predictions <- predict(m, newdata = qcs_grid, return_tmb_object = TRUE)
ind <- get_index(predictions)
if (require("ggplot2", quietly = TRUE)) {
ggplot(ind, aes(year, est)) + geom_line() +
geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4)
}
cog <- get_cog(predictions)
cog
}
#> year est lwr upr se max_gradient bad_eig coord
#> 1 2003 463.5260 446.4141 480.6380 8.730752 0.004448426 FALSE X
#> 2 2004 476.7402 466.4506 487.0298 5.249898 0.004448426 FALSE X
#> 3 2005 470.6887 457.7493 483.6281 6.601835 0.004448426 FALSE X
#> 4 2007 480.8949 464.5560 497.2338 8.336337 0.004448426 FALSE X
#> 5 2009 477.2028 457.9182 496.4874 9.839269 0.004448426 FALSE X
#> 6 2011 470.5112 457.6004 483.4221 6.587304 0.004448426 FALSE X
#> 7 2013 471.9877 455.6076 488.3677 8.357329 0.004448426 FALSE X
#> 8 2015 463.0289 449.6441 476.4136 6.829060 0.004448426 FALSE X
#> 9 2017 470.5220 455.4189 485.6251 7.705797 0.004448426 FALSE X
#> 10 2003 5757.8611 5739.8545 5775.8677 9.187187 0.004448426 FALSE Y
#> 11 2004 5732.5035 5720.8786 5744.1284 5.931175 0.004448426 FALSE Y
#> 12 2005 5763.0315 5750.1526 5775.9105 6.571026 0.004448426 FALSE Y
#> 13 2007 5738.2312 5716.8425 5759.6200 10.912839 0.004448426 FALSE Y
#> 14 2009 5734.0287 5713.3605 5754.6970 10.545221 0.004448426 FALSE Y
#> 15 2011 5747.1037 5733.6282 5760.5793 6.875406 0.004448426 FALSE Y
#> 16 2013 5747.6447 5728.9691 5766.3204 9.528583 0.004448426 FALSE Y
#> 17 2015 5753.9699 5736.8439 5771.0958 8.737907 0.004448426 FALSE Y
#> 18 2017 5755.9729 5739.6443 5772.3016 8.331093 0.004448426 FALSE Y
# }