`Assess2OM`

is identical to `VPA2OM`

.`R/VPA2OM.R`

`Assess2OM.Rd`

A function that uses a set of bootstrap estimates of numbers-at-age, fishing mortality rate-at-age, M-at-age, weight-at-age, length-at-age and Maturity-at-age to define a fully described MSEtool operating model. The user still needs to parameterize most of the observation and implementation portions of the operating model.

```
Assess2OM(
Name = "A fishery made by VPA2OM",
proyears = 50,
interval = 2,
CurrentYr = as.numeric(format(Sys.Date(), "%Y")),
h = 0.999,
Obs = MSEtool::Imprecise_Unbiased,
Imp = MSEtool::Perfect_Imp,
naa,
faa,
waa,
Mataa,
Maa,
laa,
nyr_par_mu = 3,
LowerTri = 1,
recind = 0,
plusgroup = TRUE,
altinit = 0,
fixq1 = TRUE,
report = FALSE,
silent = FALSE,
...
)
VPA2OM(
Name = "A fishery made by VPA2OM",
proyears = 50,
interval = 2,
CurrentYr = as.numeric(format(Sys.Date(), "%Y")),
h = 0.999,
Obs = MSEtool::Imprecise_Unbiased,
Imp = MSEtool::Perfect_Imp,
naa,
faa,
waa,
Mataa,
Maa,
laa,
nyr_par_mu = 3,
LowerTri = 1,
recind = 0,
plusgroup = TRUE,
altinit = 0,
fixq1 = TRUE,
report = FALSE,
silent = FALSE,
...
)
```

- Name
Character string. The name of the operating model.

- proyears
Positive integer. The number of projection years for MSE.

- interval
Positive integer. The interval at which management procedures will update the management advice in runMSE, e.g., 1 = annual updates.

- CurrentYr
Positive integer. The current year (final year of fitting to data)

- h
The steepness of the stock-recruitment curve (greater than 0.2 and less than 1, assumed to be close to 1 to match VPA assumption). Either a single numeric or a length nsim vector.

- Obs
The observation model (class Obs). This function only updates the catch and index observation error.

- Imp
The implementation model (class Imp). This function does not update implementation parameters.

- naa
Numeric array

`[sim, ages, year]`

. Numbers-at-age`[first age is age zero]`

.- faa
Numeric array

`[sim, ages, year]`

. Fishing mortality rate-at-age`[first age is age zero]`

.- waa
Numeric array

`[sim, ages, year]`

. Weight-at-age`[first age is age zero]`

.- Mataa
Numeric array

`[sim, ages, year]`

. Maturity (spawning fraction)-at-age`[first age is age zero]`

.- Maa
Numeric array

`[sim, ages, year]`

. Natural mortality rate-at-age`[first age is age zero]`

.- laa
Numeric array

`[sim, ages, year]`

. Length-at-age`[first age is age zero]`

.- nyr_par_mu
Positive integer. The number of recent years that natural mortality, age vulnerability, weight, length and maturity parameters are averaged over for defining future projection conditions.

- LowerTri
Integer. The number of recent years for which model estimates of recruitment are ignored (not reliably estimated by the assessment)

- recind
Positive integer. The first age class that fish 'recruit to the fishery'. The default is 0 - ie the first position in the age dimension of naa is age zero

- plusgroup
Logical. Does the assessment assume that the oldest age class is a plusgroup?

- altinit
Integer. Various assumptions for how to set up the initial numbers. 0: standard, 1: no plus group, 2: temporary fix for MSEtool plus group initialization

- fixq1
Logical. Should q be fixed (ie assume the F-at-age array faa is accurate?

- report
Logical, if TRUE, a diagnostic will be reported showing the matching of the OM reconstructed numbers at age vs the assessment.

- silent
Whether to silence messages to the console.

- ...
Additional arguments (for all, either a numeric or a length nsim vector):

`fecaa`

Fecundity at age. Default fecundity is the product of maturity and weight at age.`SRrel`

Stock-recruit relationship. (`1`

for Beverton-Holt (default),`2`

for Ricker)`R0`

unfished recruitment`phi0`

unfished spawners per recruit associated with R0 and h. With time-varying parameters, openMSE uses the mean phi0 in the first`ageM`

(age of 50 percent maturity) years for the stock-recruit relationship.`Assess2OM`

will re-calculate R0 and h in the operating model such that the stock-recruit`alpha`

and`beta`

parameters match values implied in the input.`Perr`

recruitment standard deviation (lognormal distribution) for sampling future recruitment`AC`

autocorrelation in future recruitment deviates.

An object of class OM.

Use a seed for the random number generator to sample future recruitment.