profile_ML {MLZ}

Grid search for the mean length estimator

Description

A grid search is performed over the time series, which can be used to identify local and global minima. A plot of the likelihood surface is also created similar to Figure 6 of Gedamke and Hoenig (2006) or Figure 3 of Huynh et al. (2017).

Usage

profile_ML(MLZ_data, ncp, startZ = rep(0.5, ncp + 1), min.time = 3,
  parallel = ifelse(ncp > 2, TRUE, FALSE), figure = TRUE,
  color = TRUE)

Arguments

MLZ_data

An object of class MLZ_data.

ncp

The number of change points.

startZ

A vector of length ncp+1 as the starting value of total mortality rate used in the grid search.

min.time

The minimum number of years between change points. Only used if ncp > 1.

parallel

Whether grid search is performed using parallel processing.

figure

If TRUE, creates a plot of the likelihood over the grid search. Only used if ncp = 1 or 2.

color

If TRUE, creates a color plot for the likelihood surface. Only used if ncp = 2.

Value

A matrix of change points with the negative log-likelihood values.

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.

Huynh, Q.C, Gedamke, T., Hoenig, J.M, and Porch C. 2017. Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.

Examples

## Not run: 
data(Goosefish)
profile_ML(Goosefish, ncp = 1)
profile_ML(Goosefish, ncp = 2)

## End(Not run)