R/get-index-sims.R
get_index_sims.Rd
Calculate a population index via simulation from the joint precision matrix.
Compared to get_index()
, this version can be dramatically faster
if bias correction was turned on in get_index()
while being approximately
equivalent. This is an experimental function. We have yet to find a model
where this function fails to provide a reasonable result, but make no
guarantees.
predict.sdmTMB()
output with nsim > 0
.
The confidence level.
Logical. Return simulation draws? The default (FALSE
) is
a quantile summary of those simulation draws.
A vector of grid cell/polyon areas for each year-grid cell (row
of data) in obj
. Adjust this if cells are not of unit area or not all
the same area (e.g., some cells are partially over land/water). Note that
the area vector is added as log(area)
to the raw values in obj
. In
other words, the function assumes a log link, which typically makes sense.
Function to summarize the estimate (the expected value).
mean()
would be an alternative to median()
.
Function to apply area weighting.
Assuming a log link, the function(x, area) x + log(area)
default makes sense.
If in natural space, function(x, area) x * area
makes sense.
Function to aggregate samples within each time slice.
Assuming a log link, the function(x) sum(exp(x))
default makes sense.
If in natural space, function(x) sum(x)
makes sense.
A data frame. If return_sims = FALSE
:
name of column (e.g. year
) that was supplied to sdmTMB()
time argument
est
: estimate
lwr
: lower confidence interval value
upr
: upper confidence interval value
log_est
: log estimate
se
: standard error on the log estimate
If return_sims = TRUE
, samples from the index values in a long-format data frame:
name of column (e.g. year
) that was supplied to sdmTMB()
time argument
.value
: sample value
.iteration
: sample number
Can also be used to produce an index from a model fit with tmbstan.
This function does nothing more than summarize and reshape the matrix of simulation draws into a data frame.
if (inla_installed()) {
m <- sdmTMB(density ~ 0 + as.factor(year) + depth_scaled + depth_scaled2,
data = pcod_2011, mesh = pcod_mesh_2011, family = tweedie(link = "log"),
time = "year"
)
qcs_grid_2011 <- subset(qcs_grid, year >= 2011)
p <- predict(m, newdata = qcs_grid_2011, nsim = 100)
x <- get_index_sims(p)
x_sims <- get_index_sims(p, return_sims = TRUE)
if (require("ggplot2", quietly = TRUE)) {
ggplot(x, aes(year, est, ymin = lwr, ymax = upr)) +
geom_line() +
geom_ribbon(alpha = 0.4)
ggplot(x_sims, aes(as.factor(year), .value)) +
geom_violin()
}
# Demo custom functions if working in natural space:
ind <- get_index_sims(
exp(p),
agg_function = function(x) sum(x),
area_function = function(x, area) x * area
)
}