The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Chained Onsets

This vignette shows how to define staged processing with after(). Here accumulator C starts only after accumulator B has finished.

library(AccumulatR)
## 
## Attaching package: 'AccumulatR'
## The following object is masked from 'package:stats':
## 
##     simulate

Define the model

A is directly observed. B is latent. C is observed and starts only after B has finished.

model <- race_spec() |>
  add_accumulator("A", "lognormal") |>
  add_accumulator("B", "lognormal") |>
  add_accumulator("C", "lognormal", onset = after("B")) |>
  add_outcome("A", "A") |>
  add_outcome("C", "C") |>
  set_parameters(separate = list(m = TRUE, s = TRUE)) |>
  finalize_model()

true_params <- c(
  A.m = log(0.28),
  A.s = 0.14,
  B.m = log(0.1),
  B.s = 0.1,
  C.m = log(0.15),
  C.s = 0.1
)

Simulate data

Each trial contributes an observed response label and response time.

set.seed(123456)

n_trials <- 2000
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)
## 
##    A    C 
##  446 1554

Estimate parameters with optim()

We estimate A.m, A.s, B.m, B.s, C.m, and C.s. The spread parameters are optimized on the log scale.

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

neg_loglik <- function(theta) {
  est <- true_params
  est[c("A.m", "A.s", "B.m", "B.s", "C.m", "C.s")] <- theta[c("A.m", "A.s", "B.m", "B.s", "C.m", "C.s")]
  est[c("A.s", "B.s", "C.s")] <- exp(est[c("A.s", "B.s", "C.s")])
  params_df <- build_param_matrix(
    model,
    est,
    n_trials = n_trials
  )
  ll <- log_likelihood(ctx, prepared, params_df)
  -as.numeric(ll)
}

start <- c(
  A.m = log(0.22),
  A.s = log(0.10),
  B.m = log(0.28),
  B.s = log(0.10),
  C.m = log(0.28),
  C.s = log(0.10)
)

fit <- optim(start, neg_loglik, method = "Nelder-Mead")

fit_params <- fit$par
fit_params[c("A.s", "B.s", "C.s")] <- exp(fit_params[c("A.s", "B.s", "C.s")])
target <- true_params[c("A.m", "A.s", "B.m", "B.s", "C.m", "C.s")]

data.frame(
  true = target,
  recovered = fit_params,
  miss = abs(target - fit_params)
)

Only the combined finishing time of B and C is observed through response C. Separating their timing parameters can therefore be difficult. Additional observations of the stages or constraints on their parameters can help identify the individual contributions.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.