A nonequilibrium Beverton-Holt estimator of instantaneous total mortality (Z) from length data.

```
bhnoneq(year=NULL,mlen=NULL, ss=NULL, K = NULL, Linf = NULL,
Lc = NULL, nbreaks = NULL, styrs = NULL, stZ = NULL,
graph = TRUE)
```

year

the vector of year values associated with mean length data. The number of year values must correspond to the number of length records. Include year value even if mean length and numbers (see below) are missing.

mlen

the vector of mean lengths for lengths >=Lc. One record for each year.

ss

the vector of numbers of observations associated with the mean length.

K

the growth coefficient from a von Bertalanffy growth model.

Linf

the L-infinity coefficient from a von Bertalanffy growth model.

Lc

the length at first capture.

nbreaks

the number of times (breaks) mortality is thought to change over the time series. Can be 0 or greater.

styrs

the starting guess(es) of the year(s) during which mortality is thought to change. The number of starting guesses must match the number of mortality breaks, should be separated by commas within the concatentation function and should be within the range of years present in the data.

stZ

the starting guesses of Z values enclosed within the concatentation function. There should be *nbreaks+1* values provided.

graph

logical indicating whether the observed vs predicted and residual plots should be drawn. Default=TRUE.

list element containing table of parameters with estimates, standard errors, and t-values.

list element specifying if convergence was reached.

list element specifying if hessian is positive definite

list element containing, observed value, predicted values, and residuals from the model fit.

The mean lengths for each year for lengths>=Lc. Following Gedamke and Hoening(2006), the model estimates `nbreaks+1`

Z values, the year(s) in which the
changes in mortality began, the standard deviation of lengths>=Lc, and standard errors of all parameters. An AIC value is produced for model comparison.
The estimated parameters for the number of `nbreaks`

is equal to `2*nbreaks+2`

. Problematic parameter estimates may have extremely large t-values or
extremely small standard error. Quang C. Huynh of Virginia Institute of Marine Science revised the function to make estimation more stable. Specifically,
the derivative method BFGS is used in `optim`

which allows more reliable convergence to the global minimum from a given set of starting values,
a function is included to estimate Z assuming equilibrium, sigma is estimated analytically and convergence results .
Use 0 `nbreaks`

to get Z equilibrium.

Gedamke, T. and J. M. Hoenig. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Trans. Am. Fish. Soc. 135:476-487

```
# NOT RUN {
data(goosefish)
bhnoneq(year=goosefish$year,mlen=goosefish$mlen, ss=goosefish$ss,
K=0.108,Linf=126,Lc=30,nbreaks=1,styrs=c(1982),stZ=c(0.1,0.3))
# }
```

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