## -----------------------------------------------------------------------------
library(AccumulatR)

## -----------------------------------------------------------------------------
model <- race_spec() |>
  add_accumulator("go1", "lognormal") |>
  add_accumulator("go2", "lognormal") |>
  add_outcome("R1", "go1") |>
  add_outcome("R2", "go2") |>
  add_trigger("shared_trigger", members = c("go1", "go2")) |>
  set_parameters(separate = list(m = TRUE, s = TRUE)) |>
  finalize_model()

true_params <- c(
  go1.m = log(0.30),
  go1.s = 0.18,
  go2.m = log(0.35),
  go2.s = 0.18,
  shared_trigger = 0.15
)

## -----------------------------------------------------------------------------
set.seed(123456)

n_trials <- 1500
params_df <- build_param_matrix(model, true_params, n_trials = n_trials)

sim <- simulate(model, params_df)

data_df <- data.frame(
  trials = sim$trials,
  R = factor(sim$R),
  rt = sim$rt,
  stringsAsFactors = FALSE
)

table(data_df$R, useNA = "ifany")

## -----------------------------------------------------------------------------
prepared <- prepare_data(model, data_df)
ctx <- make_context(model)

params_df_true <- build_param_matrix(
  model,
  true_params,
  n_trials = n_trials
)

ll_true <- as.numeric(log_likelihood(ctx, prepared, params_df_true))
ll_true

## -----------------------------------------------------------------------------
model_independent <- race_spec() |>
  add_accumulator("go1", "lognormal") |>
  add_accumulator("go2", "lognormal") |>
  add_outcome("R1", "go1") |>
  add_outcome("R2", "go2") |>
  add_trigger("go1_trigger", members = "go1") |>
  add_trigger("go2_trigger", members = "go2") |>
  set_parameters(
    separate = list(m = TRUE, s = TRUE),
    share = list(shared_trigger = c("go1_trigger", "go2_trigger"))
  ) |>
  finalize_model()

independent_prepared <- prepare_data(model_independent, data_df)
independent_ctx <- make_context(model_independent)

params_df_independent <- build_param_matrix(
  model_independent,
  true_params,
  n_trials = n_trials
)

ll_independent <- as.numeric(log_likelihood(
  independent_ctx,
  independent_prepared,
  params_df_independent
))
ll_independent

## -----------------------------------------------------------------------------
model_single_q <- race_spec() |>
  add_accumulator("go1", "lognormal") |>
  add_accumulator("go2", "lognormal") |>
  add_outcome("R1", "go1") |>
  add_outcome("R2", "go2") |>
  add_trigger("go1_trigger", members = "go1") |>
  finalize_model()

## ----eval=FALSE---------------------------------------------------------------
# neg_loglik <- function(theta) {
#   est <- true_params
#   est["go1.m"] <- theta[["go1.m"]]
#   est["go1.s"] <- exp(theta[["log_go1.s"]])
#   est["go2.m"] <- theta[["go2.m"]]
#   est["go2.s"] <- exp(theta[["log_go2.s"]])
#   params_df <- build_param_matrix(
#     model,
#     est,
#     n_trials = n_trials
#   )
#   ll <- log_likelihood(ctx, prepared, params_df)
#   -as.numeric(ll)
# }
# 
# start <- c(
#   go1.m = log(0.25),
#   log_go1.s = log(0.12),
#   go2.m = log(0.42),
#   log_go2.s = log(0.12)
# )
# 
# fit <- optim(
#   start,
#   neg_loglik,
#   method = "Nelder-Mead",
#   control = list(maxit = 4000, reltol = 1e-9)
# )
# 
# fit_params <- c(
#   go1.m = fit$par[["go1.m"]],
#   go1.s = exp(fit$par[["log_go1.s"]]),
#   go2.m = fit$par[["go2.m"]],
#   go2.s = exp(fit$par[["log_go2.s"]])
# )
# target <- true_params[c("go1.m", "go1.s", "go2.m", "go2.s")]
# 
# data.frame(true = target, recovered = fit_params, miss = abs(target - fit_params))