profile_MLCR | R Documentation |
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).
profile_MLCR(MLZ_data, ncp, CPUE.type = c(NA, "NPUE", "WPUE"), loglikeCPUE = c("normal", "lognormal"), startZ = rep(0.5, ncp + 1), min.time = 3, parallel = ifelse(ncp > 2, TRUE, FALSE), figure = TRUE, color = TRUE)
MLZ_data |
An object of class |
ncp |
The number of change points. |
CPUE.type |
Indicates whether CPUE time series is abundance or biomass based. |
loglikeCPUE |
Indicates whether the log-likelihood for the CPUE will be lognormally or normally distributed. |
startZ |
A vector of length |
min.time |
The minimum number of years between change points. Only used if |
parallel |
Whether the grid search is performed with parallel processing. |
figure |
If |
color |
If |
A matrix of change points with the total negative log-likelihood values and values from the mean lengths and catch rates.
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.
## Not run: data(MuttonSnapper) profile_MLCR(MuttonSnapper, ncp = 1, CPUE.type = 'WPUE') ## End(Not run)
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