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Get Francis Weights

do_francis_reweighting.Rd

Computes Francis composition reweighting factors for pooled and population-specific fishery and survey age and length compositions. Used inside run_francis or directly in a user-defined reweighting loop.

Usage

do_francis_reweighting(data, rep, age_labels, len_labels, year_labels)

Arguments

data

List of model data inputs containing observed compositions, usage flags, input sample sizes, weight arrays, and composition type matrices for both pooled and population-specific data streams.

rep

Report list from a fitted RTMB model containing predicted compositions (CAA, CAL, SrvIAA, SrvIAL) with a leading population dimension.

age_labels

Vector of observed age bin labels.

len_labels

Vector of observed length bin labels.

year_labels

Vector of assessment year labels.

Value

A named list containing:

new_fish_age_wts, new_fish_len_wts, new_srv_age_wts, new_srv_len_wts

Updated Francis weight arrays for pooled compositions, same dimensions as the corresponding Wt_* arrays in data.

new_fish_age_pop_wts, new_fish_len_pop_wts, new_srv_age_pop_wts, new_srv_len_pop_wts

Updated Francis weight arrays for population-specific compositions, same dimensions as the corresponding Wt_*_pop arrays in data. Cells remain NA when the corresponding Use*_pop flag contains no ones.

mean_francis

Long-format dataframe of observed and expected composition means across all data streams and populations, with columns Region, Comp_Year, Comp_Seas, Sex, Fleet, obs, pred, Type, and Pop (pooled rows have Pop = NA).

See also

Other Francis Reweighting: run_francis()

Examples

if (FALSE) { # \dontrun{
for(j in 1:5) {
  if(j == 1) {
    data$Wt_FishAgeComps[] <- 1; data$Wt_FishLenComps[] <- 1
    data$Wt_SrvAgeComps[]  <- 1; data$Wt_SrvLenComps[]  <- 1
    data$Wt_FishAgeComps_pop[] <- 0; data$Wt_FishLenComps_pop[] <- 0
    data$Wt_SrvAgeComps_pop[]  <- 0; data$Wt_SrvLenComps_pop[]  <- 0
  } else {
    data$Wt_FishAgeComps[] <- wts$new_fish_age_wts
    data$Wt_FishLenComps[] <- wts$new_fish_len_wts
    data$Wt_SrvAgeComps[]  <- wts$new_srv_age_wts
    data$Wt_SrvLenComps[]  <- wts$new_srv_len_wts
    if(any(data$UseFishAgeComps_pop == 1)) data$Wt_FishAgeComps_pop[] <- wts$new_fish_age_pop_wts
    if(any(data$UseFishLenComps_pop == 1)) data$Wt_FishLenComps_pop[] <- wts$new_fish_len_pop_wts
    if(any(data$UseSrvAgeComps_pop  == 1)) data$Wt_SrvAgeComps_pop[]  <- wts$new_srv_age_pop_wts
    if(any(data$UseSrvLenComps_pop  == 1)) data$Wt_SrvLenComps_pop[]  <- wts$new_srv_len_pop_wts
  }
  obj <- fit_model(data, parameters, mapping, random = NULL,
                   newton_loops = 3, silent = TRUE)
  rep <- obj$report(obj$env$last.par.best)
  wts <- do_francis_reweighting(data = data, rep = rep, age_labels = 2:31,
                                len_labels = seq(41, 99, 2), year_labels = 1960:2024)
}
} # }