model.R

## Run analysis, write model results

## Before: input.rds (data)
## After:  results.rds (model)

library(TAF)
suppressMessages(library(dplyr))  # arrange, filter, mutate, sample_n, ungroup
library(purrr)    # map, map_lgl, safely
library(sraplus)  # diagnose_sraplus, fit_sraplus, summarize_sralpus

mkdir("model")

stocks <- readRDS("data/input.rds")

## Run model
set.seed(1)
setwd("model")  # compile inside 'model' folder
sfs <- safely(fit_sraplus)
samps <- nrow(stocks)
a <- Sys.time()
stocks <- stocks %>%
  ungroup() %>%
  sample_n(samps) %>%
  mutate(sraplus_fit = map(
    driors,
    ~ sfs(
      driors = .x,
      engine = "stan",
      model = "sraplus_tmb",
      adapt_delta = 0.9,
      max_treedepth = 10,
      n_keep = 4000,
      chains = 1,
      cores = 1,
      ## q_slope prior=,
      estimate_qslope = FALSE,
      estimate_proc_error = TRUE,
      cleanup = TRUE)
    ))
setwd("..")
Sys.time() - a

## Add columns 'sraplus_worked', 'sraplus_fit',
## 'sraplus_summary', and 'sraplus_diagnostics'
stocks <- stocks %>%
  mutate(sraplus_worked = map_lgl(map(sraplus_fit,"error"), is.null)) %>%
  filter(sraplus_worked) %>%
  mutate(sraplus_fit = map(sraplus_fit,"result")) %>%
  mutate(sraplus_summary = map(sraplus_fit, summarize_sralpus)) %>%
  mutate(sraplus_diagnostics = map2(sraplus_fit, driors, diagnose_sraplus)) %>%
  arrange(stock)

## Export
saveRDS(stocks, "model/results.rds")