get_index_sims {sdmTMB} | R Documentation |
Calculate a population index via simulation from the joint precision matrix
Description
Calculate a population index via simulation from the joint precision matrix.
Compared to get_index()
, this version can be faster if bias correction was
turned on in get_index()
while being approximately equivalent. This is an
experimental function. This function usually works reasonably well, but we
make no guarantees. It is recommended to use get_index()
with bias_correct = TRUE
for final inference.
Usage
get_index_sims(
obj,
level = 0.95,
return_sims = FALSE,
area = rep(1, nrow(obj)),
est_function = stats::median,
area_function = function(x, area) x + log(area),
agg_function = function(x) sum(exp(x))
)
Arguments
obj |
|
level |
The confidence level. |
return_sims |
Logical. Return simulation draws? The default ( |
area |
A vector of grid cell/polyon areas for each year-grid cell (row
of data) in |
est_function |
Function to summarize the estimate (the expected value).
|
area_function |
Function to apply area weighting.
Assuming a log link, the |
agg_function |
Function to aggregate samples within each time slice.
Assuming a log link, the |
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.
Value
A data frame. If return_sims = FALSE
:
name of column (e.g.
year
) that was supplied tosdmTMB()
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 tosdmTMB()
time argument-
.value
: sample value -
.iteration
: sample number
See Also
Examples
m <- sdmTMB(density ~ 0 + as.factor(year),
data = pcod_2011, mesh = pcod_mesh_2011, family = tweedie(link = "log"),
time = "year"
)
qcs_grid_2011 <- replicate_df(qcs_grid, "year", unique(pcod_2011$year))
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
)