`sdmTMB()`

and `stats::nlminb()`

control options.

## Usage

```
sdmTMBcontrol(
eval.max = 2000L,
iter.max = 1000L,
normalize = FALSE,
nlminb_loops = 1L,
newton_loops = 1L,
mgcv = deprecated(),
quadratic_roots = FALSE,
start = NULL,
map_rf = deprecated(),
map = NULL,
lower = NULL,
upper = NULL,
censored_upper = NULL,
multiphase = TRUE,
profile = FALSE,
get_joint_precision = TRUE,
parallel = getOption("sdmTMB.cores", 1L),
...
)
```

## Arguments

- eval.max
Maximum number of evaluations of the objective function allowed.

- iter.max
Maximum number of iterations allowed.

- normalize
Logical: use

`TMB::normalize()`

to normalize the process likelihood using the Laplace approximation? Can result in a substantial speed boost in some cases. This used to default to`FALSE`

prior to May 2021. Currently not working for models fit with REML or random intercepts.- nlminb_loops
How many times to run

`stats::nlminb()`

optimization. Sometimes restarting the optimizer at the previous best values aids convergence. If the maximum gradient is still too large, try increasing this to`2`

.- newton_loops
How many Newton optimization steps to try after running

`stats::nlminb()`

. This sometimes aids convergence by further reducing the log-likelihood gradient with respect to the fixed effects. This calculates the Hessian at the current MLE with`stats::optimHess()`

using a finite-difference approach and uses this to update the fixed effect estimates.- mgcv
**Deprecated**Parse the formula with`mgcv::gam()`

?- quadratic_roots
Experimental feature for internal use right now; may be moved to a branch. Logical: should quadratic roots be calculated? Note: on the sdmTMB side, the first two coefficients are used to generate the quadratic parameters. This means that if you want to generate a quadratic profile for depth, and depth and depth^2 are part of your formula, you need to make sure these are listed first and that an intercept isn't included. For example,

`formula = cpue ~ 0 + depth + depth2 + as.factor(year)`

.- start
A named list specifying the starting values for parameters. You can see the necessary structure by fitting the model once and inspecting

`your_model$tmb_obj$env$parList()`

. Elements of`start`

that are specified will replace the default starting values.- map_rf
**Deprecated**use`spatial = 'off', spatiotemporal = 'off'`

in`sdmTMB()`

.- map
A named list with factor

`NA`

s specifying parameter values that should be fixed at a constant value. See the documentation in`TMB::MakeADFun()`

. This should usually be used with`start`

to specify the fixed value.- lower
An optional named list of lower bounds within the optimization. Parameter vectors with the same name (e.g.,

`b_j`

or`ln_kappa`

in some cases) can be specified as a numeric vector. E.g.`lower = list(b_j = c(-5, -5))`

.- upper
An optional named list of upper bounds within the optimization.

- censored_upper
An optional vector of upper bounds for

`sdmTMBcontrol()`

. Values of`NA`

indicate an unbounded right-censored to distribution, values greater that the observation indicate and upper bound, and values equal to the observation indicate no censoring.- multiphase
Logical: estimate the fixed and random effects in phases? Phases are usually faster and more stable.

- profile
Logical: should population-level/fixed effects be profiled out of the likelihood? These are then appended to the random effects vector without the Laplace approximation. See

`TMB::MakeADFun()`

.*This can dramatically speed up model fit if there are many fixed effects but is experimental at this stage.*- get_joint_precision
Logical. Passed to

`getJointPrecision`

in`TMB::sdreport()`

. Must be`TRUE`

to use simulation-based methods in`predict.sdmTMB()`

or`[get_index_sims()]`

. If not needed, setting this`FALSE`

will reduce object size.- parallel
Argument currently ignored. For parallel processing with 3 cores, as an example, use

`TMB::openmp(n = 3, DLL = "sdmTMB")`

. But be careful, because it's not always faster with more cores and there is definitely an upper limit.- ...
Anything else. See the 'Control parameters' section of

`stats::nlminb()`

.

## Details

Usually used within `sdmTMB()`

. For example:

## Examples

```
sdmTMBcontrol()
#> $eval.max
#> [1] 2000
#>
#> $iter.max
#> [1] 1000
#>
#> $normalize
#> [1] FALSE
#>
#> $nlminb_loops
#> [1] 1
#>
#> $newton_loops
#> [1] 1
#>
#> $profile
#> [1] FALSE
#>
#> $quadratic_roots
#> [1] FALSE
#>
#> $start
#> NULL
#>
#> $map
#> NULL
#>
#> $lower
#> NULL
#>
#> $upper
#> NULL
#>
#> $censored_upper
#> NULL
#>
#> $multiphase
#> [1] TRUE
#>
#> $parallel
#> [1] 1
#>
#> $get_joint_precision
#> [1] TRUE
#>
```