spmR canonical examples

library(spmR)
library(readr)

This vignette is intentionally limited to two canonical example directories that are provided with the package for reading archived output:

• examples/atka for runSPM(), dat2list(), and plotSPMx()
• examples/BSRE_AI for plotSPM() with spm_summary.csv

The bundled positional files retain their historical layout. For a new run, create metadata with explicit recruitment basis and female/male population weights, then use a format-2 executable. Follow the runnable split-sex input migration guide. Keep each species file’s existing field order and store added population vectors in the metadata.

1. Atka workflow (examples/atka)

Use runSPM() to read existing ADMB output (spm_detail.csv) and inspect inputs using dat2list().

pkg_root <- if (file.exists("DESCRIPTION")) "." else ".."
atka_dir <- file.path(pkg_root, "examples", "atka")

atka_detail <- runSPM(atka_dir, run = FALSE, engine = "admb")
str(atka_detail)
#> spm_rslt [105,000 × 20] (S3: spm_result/spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ Stock      : chr [1:105000] "Model_16.0b" "Model_16.0b" "Model_16.0b" "Model_16.0b" ...
#>  $ Alt        : num [1:105000] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Sim        : num [1:105000] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Year       : num [1:105000] 2022 2023 2024 2025 2026 ...
#>  $ SSB        : num [1:105000] 137805 122551 111309 106528 107685 ...
#>  $ Rec        : num [1:105000] 648 518 358 529 1080 ...
#>  $ Tot_biom   : num [1:105000] 631455 619958 620871 620648 586312 ...
#>  $ SPR_Implied: num [1:105000] 0.517 0.444 0.455 0.413 0.41 ...
#>  $ F          : num [1:105000] 0.372 0.504 0.482 0.576 0.583 ...
#>  $ Ntot       : num [1:105000] 551 514 473 473 483 ...
#>  $ Catch      : num [1:105000] 66481 83800 73495 83297 82317 ...
#>  $ ABC        : num [1:105000] 102578 98592 86706 83297 82317 ...
#>  $ OFL        : num [1:105000] 123759 118791 101474 97783 96860 ...
#>  $ AvgAge     : num [1:105000] 4.73 4.52 4.22 4.06 4.19 ...
#>  $ AvgAgeTot  : num [1:105000] 2.81 2.79 2.97 2.81 2.28 ...
#>  $ SexRatio   : num [1:105000] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
#>  $ B100       : num [1:105000] 280456 280456 280456 280456 280456 ...
#>  $ B40        : num [1:105000] 112182 112182 112182 112182 112182 ...
#>  $ B35        : num [1:105000] 98160 98160 98160 98160 98160 ...
#>  $ Scenario   : chr [1:105000] "1" "1" "1" "1" ...
#>  - attr(*, "spec")=
#>   .. cols(
#>   ..   Stock = col_character(),
#>   ..   Alt = col_double(),
#>   ..   Sim = col_double(),
#>   ..   Year = col_double(),
#>   ..   SSB = col_double(),
#>   ..   Rec = col_double(),
#>   ..   Tot_biom = col_double(),
#>   ..   SPR_Implied = col_double(),
#>   ..   F = col_double(),
#>   ..   Ntot = col_double(),
#>   ..   Catch = col_double(),
#>   ..   ABC = col_double(),
#>   ..   OFL = col_double(),
#>   ..   AvgAge = col_double(),
#>   ..   AvgAgeTot = col_double(),
#>   ..   SexRatio = col_double(),
#>   ..   B100 = col_double(),
#>   ..   B40 = col_double(),
#>   ..   B35 = col_double()
#>   .. )
#>  - attr(*, "problems")=<pointer: 0x5617b250da10>

atka_inputs <- dat2list(file.path(atka_dir, "spm.dat"))
names(atka_inputs)
#>  [1] "rn"               "Tier"             "nalts"            "alts"            
#>  [5] "tac_flag"         "srr_type"         "srr_form"         "srr_conditioning"
#>  [9] "srr_reserved"     "spm_detail_flag"  "nprj_yrs"         "nsims"           
#> [13] "beg_yr"           "nyrs_fixed_catch" "nspp"             "OY_min"          
#> [17] "OY_max"           "datafile"         "ABC_mults"        "scalars"         
#> [21] "alt4_spr"         "nTAC_cat"         "nTACind"          "fixed_catch"

Plot detailed simulation trajectories with plotSPMx().

plotSPMx(atka_detail, alt = 2, thisyr = min(atka_detail$Year), mytitle = "Atka detail plot")

Archived experimental RTMB output can also be read from this directory. New RTMB projections are blocked because the prototype lacks population dynamics. New ADMB projections require write_spm_metadata() and a format-2 executable.

runSPM(atka_dir, run = FALSE, engine = "rtmb")

2. Summary workflow (examples/BSRE_AI)

plotSPM() expects summary-format data (spm_summary.csv).

bsre_dir <- file.path(pkg_root, "examples", "BSRE_AI")
bsre_summary <- read_csv(file.path(bsre_dir, "spm_summary.csv"))
plotSPM(bsre_summary, alt = c(1, 3, 5, 7), mytitle = "BSRE_AI summary plot")