README.md

In BenWilliams-NOAA/rockfishr:

rockfishr

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rockfishr

The goal of rockfishr is to create a clear workflow for pulling and cleaning data for rockfish fishery stock assessments based upon the ADMB rockfish assessment model. It utilizes a "project oriented workflow" via RStudio. You must be able to have a connection to the AFSC & AKFIN (Answers) data servers (e.g., VPN if offsite), and have usernames/passwords setup.

Installation

You can install the released version of datacall from github with:

# install.packages("devtools")
devtools::install_github("BenWilliams/rockfishr")

Example for 2020 assessment

library(datacall)

# globals ----

species = "NORK" # northern rockfish
year = 2020 # assessment year
afsc_user = "your_afsc_username"
afsc_pwd = "your_afsc_password"
akfin_user = "your_akfn_username"
akfin_pwd = "your_akfin_password"

admb_home = "C:/Program Files (x86)/ADMB-12.1" # location ADMB exists on *my* computer
TAC = c(3786, 3681, 4528) # last three years of TAC (year-3, year-2, year-1)
rec_age = 2 # recruitment age
plus_age = 45 # plus group age
model_name = "updated_nr"
dat_name = "goa_nr_2020"

# setup folders -----
modeldir(year)

# query databases ----
raw_data(species, year, afsc_user, afsc_pwd, akfin_user, akfin_pwd)

The initial output will be raw data pulls that will be placed in the year/data/raw folder.

Then clean up the raw data which will be placed in the year/data/output folder.

# catch and biomass data ----
# note: must provide file for VAST or DB estimates are output
# design-based model
survey_biomass(year) 
concat_dat(year, "db", rec_age, plus_age)

# VAST "bridge model"
survey_biomass(year, "VAST_estimate_mesa.csv") 
concat_dat(year, "m18.2", rec_age, plus_age)

# VAST GAP assessment
survey_biomass(year, "VAST_estimates.csv") 
concat_dat(year, "m18.2a", rec_age, plus_age)

# new age-error matrix ----
# rerun functions as some are dependent on age error matrix output
# updated (as of 2020-10) reader_tester file
ageage(reader_tester = NULL, species, year, admb_home) # read_tester file is provide in the "user_input" folder
fish_age_comp(year, rec_age, plus_age)
survey_age_comp(year, rec_age, plus_age)
fish_size_comp(year, rec_age)
survey_size_comp(year)
size_at_age(year, admb_home, rec_age)
weight_at_age(year, admb_home, rec_age)
concat_dat(year, "m18.2b", rec_age, plus_age)

# run models ----

Run ADMB for each model. This is best done through the command line for each model desired. admb updated_nr updated_nr - mcmc 10000000 mcsave 2000 updated_nr -mceval

Process model results and create figures.

# explore multiple outputs ----
model = "db"
mcmc = 10000000 
mcsave = 2000

process_results(year, model, model_name, data_name, rec_age, plus_age, mcmc, mcsave)
base_plots(year, model, model_name, rec_AGE)

model = "m18.2"
process_results(year, model, model_name, data_name, rec_age, plus_age, mcmc, mcsave, survey = "VAST_estimate_mesa.csv")
base_plots(year, model, model_name, rec_AGE)

model = "m18.2a"
process_results(year, model, model_name, data_name, rec_age, plus_age, mcmc, mcsave, survey = "VAST_estimates.csv")
base_plots(year, model, model_name, rec_AGE)

model = "m18.2b"
process_results(year, model, model_name, data_name, rec_age, plus_age, mcmc, mcsave, survey = "VAST_estimates.csv")
base_plots(year, model, model_name, rec_AGE)

Run the retrospective model

# run retro ----
run_retro(year, model = "m18.2b", tpl_name = "updated_nr", n_retro = 10, admb_home = admb_home)

Clean up model results.

process_retro(year, model, model_name, dat_name, rec_age, plus_age, mcmcm = 10000000, mcsave = 2000, survey = "VAST_estimates.csv"")

BenWilliams-NOAA/rockfishr documentation built on March 1, 2021, 11:12 p.m.