library(groundfishr)
# globals ----
year = 2021
species <- "SABL"
rec_age <- 3
plus_age <- 42
TAC <- c(14957, 15068, 18293) # TAC from last 3 years (year-3, year-2, year-1)
admb_home <- "C:/Program Files (x86)/ADMB-12.1" # more common to be "C/ADMB"
model_name <- "sabl"
dat_name <- "sabl_2021"
afsc_user = "your_user_name"
afsc_pwd = "your_password"
akfin_user = "your_user_name"
akfin_pwd = "your_password"
mcmc = 1e+06
mcsave = 2000
# data ----
# setup folders
setup(year)
# raw data query
sablefish(year, akfin_user, akfin_pwd, afsc_user, afsc_pwd)
# clean and process data ----
# catch
clean_catch(year = year, TAC = TAC)
# survey biomass
ts_biomass(year) # this is the design-based model, must provide separate file for VAST output
# biological data
age_error( species = species, year = year, admb_home = admb_home) # file in data/user_input folder
size_at_age(year, admb_home, rec_age, lenbins = NULL)
fish_age_comp(year, rec_age, plus_age)
ts_age_comp(year, rec_age, plus_age)
fsh_length_comp(year, rec_age)
ts_length_comp(year)
saa(year, admb_home, rec_age)
waa(year, admb_home, rec_age)
# create data file in the "db" folder
concat_dat(year, "db", rec_age, plus_age)
# change the survey to the VAST estimated
ts_biomass(year, "VAST_estimate.csv") # file in data/user_input folder
concat_dat(year, "vast", rec_age, plus_age)
run_admb(year, model, model_name, mcmc, mcsave)
# cleanup output ----
process_results(year, model = "db", model_name, data_name, rec_age, plus_age, mcmc = mcmc, mcsave = mcsave)
process_results(year, model = "vast", model_name, data_name, rec_age, plus_age, mcmc = mcmc, mcsave = mcsave)
# create base plots and tables
base_plots(year, model, model_name, rec_age)
base_tables(year, model, model_name)