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[fix]: Rceattle converter publishes wrong composition, age and uncertainty values

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#405 0 bình luận 1 reaction 0 người được giao Xem trên GitHub

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Độ khó
4/5
Thời gian dự kiến
3-5 ngày
Mức phù hợp với người mới
18/100
Loại issue
Lỗi
Độ rõ ràng
Khá rõ ràng
Mức độ hoạt động
Sôi nổi
Công nghệ
r
Lĩnh vực
data-visualization

Hướng nghiên cứu

Start with the Rceattle converter that builds the standardized output, reading quantities$comp_hat, quantities$comp_obs and quantities$age_hat, plus the filter_data() uncertainty branch and the plot_index() guard list. Each of the six defects is a separate, arguably maintainer-level decision about what the standard means, so the issue does not name one contained fix. Done would mean the converted GOAatf output has correctly dimensioned comp predictions, a valid age column, a standard uncertainty label, and plots that resolve to the right series.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.

Mô tả

Found while tracing the Rceattle branch for #400. These are pre-existing lines that the repair in #401 makes execute for the first time, so none is a regression — but the first three publish wrong numbers rather than missing ones. Measured on a GOAatf fit (1 species, 2 sexes, nages = 21, 1961–2050, 59 composition rows).

1. Predicted composition is in the age dimension for length comps. Predictions come from quantities$age_hat, predicted catch at true age, and are used for every row of comp_data. 14 rows are age comps (42 bins) and 45 are length comps (52 bins), so 2,340 of the 2,928 observed values get age-dimensioned predictions. quantities$comp_hat is the aligned quantity — each observation's own dimension, one-to-one with comp_data, rows summing to 1 — and its observed counterpart is quantities$comp_obs. Note filter(estimate != 0) hides the mismatch, because age_hat[, 43:52] is exactly 0 on a length row. The two series are also incomparable in scale even on the age rows: observed row sums run 194–3.6e9 against predicted 1.5e6–2.2e6.

2. Composition rows publish an age the model does not have. Every comp row is joint-sex (Sex = 3), so a comp vector is sex 1's bins followed by sex 2's. Both go into age as bin ordinals, giving age up to 52 for a 21-age model on 2,928 of 5,406 rows. Because age0_length1 is dropped, nothing in the output records which rows are length bins or which sex a bin belongs to, so it cannot be repaired downstream.

3. uncertainty_label = "log_sd" is not in the standard vocabulary. 214 rows carry it, and filter_data() branches on grepl("sd", ...), so it takes the sd branch and builds estimate ± uncertainty on the natural scale. An index observation of 1,112,215 with a log-scale sd of 0.064 gets a band of ±0.06 instead of 981,047–1,260,920. The SS3 vocabulary is sd/se/std/stddev. Separately the predicted row inherits the observation's log_sd, so index_predicted publishes observation error as estimation error.

4. plot_index() resolves to the series with no years. The output carries log_index_hat (module sdrep, 17 values, all year = NA) and index_observed/index_predicted (module data_list, correct years). The module disambiguator picks sdrep, so the figure gets 17 points at year = NA. The existing guard label %notin% c("index_hat", "catch_h") does not list log_index_hat.

5. Projection years publish a catch of 0. catch_hat is only computed on hindcast rows, so the 32 projection years carry catch_predicted = 0 against catch = NA — a landings figure shows the fishery stopping in 2019.

6. A held-out observation publishes a negative year. Rceattle's convention for carrying an observation but excluding it from the likelihood is a negative Year, which converts to year = -1984 with era = "time", putting a point at −1984 on any index figure.

Related: only sdrep and data_list reach the output. The converter emits those two modules, so quantities never reaches the standardized output — that is where n_at_age, biomass_at_age, m_at_age, f_at_age, sel, b0, flimit and ftarget live, ~1.6M rows and 92 labels on this fit. Consequently plot_landings(), plot_catch_comp() and plot_abundance_at_age() fail on converted Rceattle output (landings not found., abundance not found.) while plot_biomass(), plot_spawning_biomass(), plot_recruitment(), plot_index() and plot_recruitment_deviations() return figures. It is also why 31 of the 35 unit cells corrected in #401 are currently dormant, and why the catch_hat → landings mapping in rceattle_var_names.csv never fires.

Happy to take any of these on — I held off in #401 because each is a decision about what the standard means rather than a repair.

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