likelihood_tagging_ckmr.Rd*Experimental* CKMR tagging likelihood
g3l_tagging_ckmr(
nll_name,
obs_data,
fleets,
parent_stocks,
offspring_stocks,
weight = g3_parameterized(paste0(nll_name, "_weight"),
optimise = FALSE, value = 1),
run_at = g3_action_order$likelihood)Character string, used to define the variable name for obsstock and modelstock.
Data.frame of observed mother-offspring pairs with columns year / parent_age / offspring_age / mo_pairs
A list of g3_stock objects to collect catch data for.
A list of g3_stock objects that are parents in a g3a_spawn action
A list of g3_stock objects that are output_stocks in a g3a_spawn action
Weighting applied to this likelihood component. Default is a g3_param
that defaults to 1, allowing weights to be altered without recompiling.
Integer order that actions will be run within model, see g3_action_order.
Implementation of CKMR based on Bravington, M.V., Skaug, H.J., & Anderson, E.C. (2016). Close-Kin Mark-Recapture. Statistical Science, 31, 259-274.
Only one kinship probability is implemented, mother-offspring with lethal sampling, i.e. (3.2) in the paper. This is then used as a pseudo-likelihood as per (4.1).
The obs_data data.frame provides observed pairs. Unlike other likelihood mehthods, it has a fixed structure:
Year of observation for the data point.
Age of the parent in an observed parent-offspring pair.
Age of the offspring in an observed parent-offspring pair.
Number of pairs observed with these ages.
Bravington, M.V., Skaug, H.J., & Anderson, E.C. (2016). Close-Kin Mark-Recapture. Statistical Science, 31, 259-274.
g3_stock
An action (i.e. list of formula objects) that will...
For all parent_stocks and offspring_stocks, collect spawing rate into modelhist__spawning and modelhist__spawned, total number of parents and total number of spawned offspring respectively
For all fleets, collect catch data into modelhist__catch
For any observed pairs that year, include the probability of that event happening into nll