ML {MLZ}

Mean length-based mortality estimator

Description

Estimator of instantaneous total mortality (Z) from a time series of mean length data.

Usage

ML(MLZ_data, ncp, start = NULL, grid.search = TRUE,
  parallel = ifelse(ncp > 2, TRUE, FALSE), min.time = 3, Z.max = 5,
  figure = TRUE)

Arguments

MLZ_data

An object of class MLZ_data containing mean lengths and life history data of stock.

ncp

The number of change points in total mortality in the time series. ncp + 1 total mortality rates will be estimated.

start

An optional list of starting values. See details.

grid.search

If TRUE, a grid search will be performed using the profile_ML function to find the best starting values for the change points (the years when mortality changes). Ignored if ncp = 0 or if start is provided.

parallel

Whether grid search is performed with parallel processing. Ignored if grid.search = FALSE.

min.time

The minimum number of years between each change point for the grid search, passed to profile_ML. Not used if grid.search = FALSE.

Z.max

The upper boundary for Z estimates.

figure

If TRUE, a call to plot of observed and predicted mean lengths will be produced.

Details

For a model with I change points, the starting values in start is a list with the following entries: Z a vector of length = I+1. yearZ a vector of length = I.

start can be NULL, in which case, the supplied starting values depend on the value of grid.search. If grid.search = TRUE, starting values will use the values for yearZ which minimize the negative log-likelihood from the grid search. Otherwise, the starting values for yearZ evenly divide the time series.

Value

An object of class MLZ_model.

References

Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135:476-487.

See Also

profile_ML

Examples

## Not run: 
data(Goosefish)
res <- ML(Goosefish, ncp = 2)
res <- ML(Goosefish, ncp = 2, start = list(Z = c(0.1, 0.3, 0.5), yearZ = c(1978, 1988)))
res <- ML(Goosefish, ncp = 2, grid.search = TRUE)

## End(Not run)