Iterative reweighting for Gadget models An implementation of the iterative reweigthing of likelihood components in gadget. It analyzes a given gadget model and, after a series of optimisations where each likelihood component is heavily weigthed, suggests a weigthing for the components based on the respective variance. If one (or more) components, other than understocking and penalty, are 0 then the gadget optimisation with the final weights will not be completed. In Taylor et. al an objective reweighting scheme for likelihood components is described for cod in Icelandic waters. The authors nota that the issue of component weighting has been discussed for some time, as the data sources have different natural scales (e.g g vs. kg) that should not affect the outcome. A simple heuristic, where the weights are the inverse of the initial sums of squares for the respective component resulting in an initials score equal to the number of components, is therfor often used. This has the intutitive advantage of all components being normalised. There is however a drawback to this since the component scores, given the initial parametrisation, are most likely not equally far from their respective optima resulting in sub-optimal weighting. The iterative reweighting heuristic tackles this problem by optimising each component separately in order to determine the lowest possible value for each component. This is then used to determine the final weights. The resoning for this approach is as follows: Conceptually the likelihood components can be thought of as residual sums of squares, and as such their variance can be esimated by dividing the SS by the degrees of freedom. The optimal weighting strategy is the inverse of the variance. Here the iteration starts with assigning the inverse SS as the initial weight, that is the initial score of each component when multiplied with the weight is 1. Then an optimisation run for each component with the intial score for that component set to 10000. After the optimisation run the inverse of the resulting SS is multiplied by the effective number of datapoints and used as the final weight for that particular component. The effective number of datapoints is used as a proxy for the degrees of freedom is determined from the number of non-zero datapoints. This is viewed as satisfactory proxy when the dataset is large, but for smaller datasets this could be a gross overestimate. In particular, if the surveyindices are weigthed on their own while the yearly recruitment is esimated they could be overfitted. If there are two surveys within the year Taylor et. al suggest that the corresponding indices from each survey are weigthed simultaneously in order to make sure that there are at least two measurement for each yearly recruit, this is done through component grouping which is implemented. Another approach, which is also implemented, for say a single survey fleet the weight for each index component is estimated from a model of the form $$\log(I_{lts}) = \mu + Y_t + \lambda_l + \Sigma_s + \epsilon_{lts}$$ where the residual term, $\epsilon_{lts}$, is independent normal with variance $\sigma_{ls}^2$. The inverse of the estimated variance from the above model as the weights between the surveyindices. After these weights have been determined all surveyindices are weighted simultaneously.

gadget_iterative_stage_1.Rd

Iterative reweighting for Gadget models

An implementation of the iterative reweigthing of likelihood components in gadget. It analyzes a given gadget model and, after a series of optimisations where each likelihood component is heavily weigthed, suggests a weigthing for the components based on the respective variance. If one (or more) components, other than understocking and penalty, are 0 then the gadget optimisation with the final weights will not be completed.

In Taylor et. al an objective reweighting scheme for likelihood components is described for cod in Icelandic waters. The authors nota that the issue of component weighting has been discussed for some time, as the data sources have different natural scales (e.g g vs. kg) that should not affect the outcome. A simple heuristic, where the weights are the inverse of the initial sums of squares for the respective component resulting in an initials score equal to the number of components, is therfor often used. This has the intutitive advantage of all components being normalised. There is however a drawback to this since the component scores, given the initial parametrisation, are most likely not equally far from their respective optima resulting in sub-optimal weighting. The iterative reweighting heuristic tackles this problem by optimising each component separately in order to determine the lowest possible value for each component. This is then used to determine the final weights. The resoning for this approach is as follows: Conceptually the likelihood components can be thought of as residual sums of squares, and as such their variance can be esimated by dividing the SS by the degrees of freedom. The optimal weighting strategy is the inverse of the variance. Here the iteration starts with assigning the inverse SS as the initial weight, that is the initial score of each component when multiplied with the weight is 1. Then an optimisation run for each component with the intial score for that component set to 10000. After the optimisation run the inverse of the resulting SS is multiplied by the effective number of datapoints and used as the final weight for that particular component. The effective number of datapoints is used as a proxy for the degrees of freedom is determined from the number of non-zero datapoints. This is viewed as satisfactory proxy when the dataset is large, but for smaller datasets this could be a gross overestimate. In particular, if the surveyindices are weigthed on their own while the yearly recruitment is esimated they could be overfitted. If there are two surveys within the year Taylor et. al suggest that the corresponding indices from each survey are weigthed simultaneously in order to make sure that there are at least two measurement for each yearly recruit, this is done through component grouping which is implemented. Another approach, which is also implemented, for say a single survey fleet the weight for each index component is estimated from a model of the form $$\log(I_{lts}) = \mu + Y_t + \lambda_l + \Sigma_s + \epsilon_{lts}$$ where the residual term, $\epsilon_{lts}$, is independent normal with variance $\sigma_{ls}^2$. The inverse of the estimated variance from the above model as the weights between the surveyindices. After these weights have been determined all surveyindices are weighted simultaneously.

gadget_iterative_stage_1(
  gd,
  grouping = list(),
  wgts = "WGTS",
  params.in = "params.in",
  ...
)

gadget_iterative_stage_2(variants, cv_floor = 0)

Arguments

gd: gadget directory
grouping: a list naming the groups of components that should be reweighted together.
wgts: a string containing the path the folder where the interim weighting results should be stored.
params.in: a string containing the location of the input parameters
...: passed to gadget_evaluate
variants: list of gadget directories
cv_floor: minimum value for the survey CV

Value

list of gadget variant dirs

gadget directory

Functions

gadget_iterative_stage_2:

Examples



if (FALSE) {
gd <- gadget.variant.dir('01-base')

gadget_iterative_stage_1(gd, params.in = 'params.in') %>% 
  parallel::mclapply(gadget_optimize, mc.cores = parallel::detectCores()) %>% 
  gadget_iterative_stage_2() %>% 
  gadget_optimize()
}