`simulate.sdmTMB`

is an S3 method for producing a matrix of simulations from
a fitted model. This is similar to `lme4::simulate.merMod()`

and
`glmmTMB::simulate.glmmTMB()`

. It can be used with the DHARMa package
among other uses.

## Usage

```
# S3 method for class 'sdmTMB'
simulate(
object,
nsim = 1L,
seed = sample.int(1e+06, 1L),
type = c("mle-eb", "mle-mvn"),
model = c(NA, 1, 2),
re_form = NULL,
mcmc_samples = NULL,
silent = TRUE,
...
)
```

## Arguments

- object
sdmTMB model

- nsim
Number of response lists to simulate. Defaults to 1.

- seed
Random number seed

- type
How parameters should be treated.

`"mle-eb"`

: fixed effects are at their maximum likelihood (MLE) estimates and random effects are at their empirical Bayes (EB) estimates.`"mle-mvn"`

: fixed effects are at their MLEs but random effects are taken from a single approximate sample. This latter option is a suggested approach if these simulations will be used for goodness of fit testing (e.g., with the DHARMa package).- model
If a delta/hurdle model, which model to simulate from?

`NA`

= combined,`1`

= first model,`2`

= second mdoel.- re_form
`NULL`

to specify a simulation conditional on fitted random effects (this only simulates observation error).`~0`

or`NA`

to simulate new random affects (smoothers, which internally are random effects, will not be simulated as new).- mcmc_samples
An optional matrix of MCMC samples. See

`extract_mcmc()`

in the sdmTMBextra package.- silent
Logical. Silent?

- ...
Extra arguments (not used)

## Examples

```
# start with some data simulated from scratch:
set.seed(1)
predictor_dat <- data.frame(X = runif(300), Y = runif(300), a1 = rnorm(300))
mesh <- make_mesh(predictor_dat, xy_cols = c("X", "Y"), cutoff = 0.1)
dat <- sdmTMB_simulate(
formula = ~ 1 + a1,
data = predictor_dat,
mesh = mesh,
family = poisson(),
range = 0.5,
sigma_O = 0.2,
seed = 42,
B = c(0.2, -0.4) # B0 = intercept, B1 = a1 slope
)
fit <- sdmTMB(observed ~ 1 + a1, data = dat, family = poisson(), mesh = mesh)
# simulate from the model:
s1 <- simulate(fit, nsim = 300)
dim(s1)
#> [1] 300 300
# test whether fitted models are consistent with the observed number of zeros:
sum(s1 == 0)/length(s1)
#> [1] 0.3297667
sum(dat$observed == 0) / length(dat$observed)
#> [1] 0.3466667
# simulate with random effects sampled from their approximate posterior
s2 <- simulate(fit, nsim = 1, params = "mle-mvn")
# these may be useful in conjunction with DHARMa simulation-based residuals
# simulate with new random fields:
s3 <- simulate(fit, nsim = 1, re_form = ~ 0)
```