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.

```
get_index_sims(
obj,
level = 0.95,
return_sims = FALSE,
area = rep(1, nrow(obj)),
est_function = stats::median,
agg_function = function(x) sum(exp(x))
)
```

## Arguments

- obj
`predict.sdmTMB()`

output with `sims > 0`

.

- level
The confidence level.

- return_sims
Logical. Return simulation draws? The default (`FALSE`

) is
a quantile summary of those simulation draws.

- area
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.

- est_function
Function to summarize the estimate (the expected value).
`mean()`

would be an alternative to `median()`

.

- agg_function
Function to aggregate samples within each time slice.
Assuming a log link, the `sum(exp(x) * area)`

default makes sense.

## Details

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.

## Examples

```
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, sims = 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()
}
}
```