MLZ (version 0.1.5)

ML: 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)

Value

An object of class MLZ_model.

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.

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

if (FALSE) {
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)
}