get_index | R Documentation |
Extract a relative biomass/abundance index, center of gravity, or effective area occupied
get_index(
obj,
bias_correct = FALSE,
level = 0.95,
area = 1,
silent = TRUE,
...
)
get_cog(
obj,
bias_correct = FALSE,
level = 0.95,
format = c("long", "wide"),
area = 1,
silent = TRUE,
...
)
get_eao(obj, bias_correct = FALSE, level = 0.95, area = 1, silent = TRUE, ...)
obj |
Output from |
bias_correct |
Should bias correction be implemented |
level |
The confidence level. |
area |
Grid cell area. A vector of length |
silent |
Silent? |
... |
Passed to |
format |
Long or wide. |
For get_index()
:
A data frame with a columns for time, estimate, lower and upper
confidence intervals, log estimate, and standard error of the log estimate.
For get_cog()
:
A data frame with a columns for time, estimate (center of gravity in x and y
coordinates), lower and upper confidence intervals, and standard error of
center of gravity coordinates.
For get_eao()
:
A data frame with a columns for time, estimate (effective area occupied; EAO),
lower and upper confidence intervals,
log EAO, and standard error of the log EAO estimates.
Geostatistical model-based indices of abundance (along with many newer papers):
Shelton, A.O., Thorson, J.T., Ward, E.J., and Feist, B.E. 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Canadian Journal of Fisheries and Aquatic Sciences 71(11): 1655–1666. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1139/cjfas-2013-0508")}
Thorson, J.T., Shelton, A.O., Ward, E.J., and Skaug, H.J. 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES J. Mar. Sci. 72(5): 1297–1310. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/icesjms/fsu243")}
Geostatistical model-based centre of gravity:
Thorson, J.T., Pinsky, M.L., and Ward, E.J. 2016. Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity. Methods Ecol Evol 7(8): 990–1002. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.12567")}
Geostatistical model-based effective area occupied:
Thorson, J.T., Rindorf, A., Gao, J., Hanselman, D.H., and Winker, H. 2016. Density-dependent changes in effective area occupied for sea-bottom-associated marine fishes. Proceedings of the Royal Society B: Biological Sciences 283(1840): 20161853. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1098/rspb.2016.1853")}
Bias correction:
Thorson, J.T., and Kristensen, K. 2016. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fisheries Research 175: 66–74. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fishres.2015.11.016")}
get_index_sims()
library(ggplot2)
# 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")
# fit a spatiotemporal model:
m <- sdmTMB(
data = pcod,
formula = density ~ 0 + as.factor(year),
time = "year", mesh = pcod_spde, family = tweedie(link = "log")
)
# prepare a prediction grid:
nd <- replicate_df(qcs_grid, "year", unique(pcod$year))
# Note `return_tmb_object = TRUE` and the prediction grid:
predictions <- predict(m, newdata = nd, return_tmb_object = TRUE)
# biomass index:
ind <- get_index(predictions)
ind
ggplot(ind, aes(year, est)) + geom_line() +
geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
ylim(0, NA)
# center of gravity:
cog <- get_cog(predictions, format = "wide")
cog
ggplot(cog, aes(est_x, est_y, colour = year)) +
geom_point() +
geom_linerange(aes(xmin = lwr_x, xmax = upr_x)) +
geom_linerange(aes(ymin = lwr_y, ymax = upr_y)) +
scale_colour_viridis_c()
# effective area occupied:
eao <- get_eao(predictions)
eao
ggplot(eao, aes(year, est)) + geom_line() +
geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.4) +
ylim(0, NA)
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